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Intelligent Metaverse Technologies & Applications (iMETA), International Conference on

作者： Muhammad Tukur Yehia Boraey Sara Jashari Alberto Jaspe Villanueva Uzair Shah Mahmood Al-Zubaidi Giovanni Pintore Enrico Gobbetti Jens Schneider Marco Agus Noora Fetais College of Science and Engineering HBKU Doha Qatar Department of Computer Science Qatar University Doha Qatar VCCVIS High Perf. Visualization KAUST Jeddah Saudi Arabia Visual and Data-intensive Computing CRS4 Cagliari Italy

ISBN: (数字)9798350351514

ISBN: (纸本)9798350351521

We discuss virtual staging technologies, focusing on two primary pipelines for creating and exploring immersive indoor environments in the metavers: an AI-based image processing pipeline and a LIDAR-based pipeline. The AI-based image processing pipeline leverages advanced AI algorithms for tasks such as clutter removal, semantic style transfer, and super-resolution, enabling rapid generation of high-quality, photorealistic virtual environments from single panoramic images. The LIDAR-based pipeline captures measurable 3D models of indoor spaces, facilitating immersive editing and collaborative design through real-time interaction with high-fidelity virtual environments. A qualitative comparative analysis of these technologies highlights their strengths and limitations in various applications. The practical implications of these pipelines are discussed, particularly their potential to transform industries such as real estate, furniture retail, interior design, construction, remote collaboration, and immersive training. The paper concludes with suggestions for future research, including conducting user studies, integrating the two pipelines, and optimizing technologies for mobile and edge devices to enhance accessibility and usability.

关键词： Training Solid modeling Three-dimensional displays Image processing Pipelines Superresolution Semantics Collaboration Transforms Usability

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Assessing the Feasibility of Incorporating AI for Efficient data Access Strategies

Assessing the Feasibility of Incorporating AI for Efficient ...

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Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Dhyan Yadav Geetha G Elackiya R Santosh D. Kumar Ritu Sharma Romil Jain Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India School of Computer Science and Engineering Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Institute of Engineering and technology Chitkara University Punjab India

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

The use of AI in statistics gets entry to techniques has recently emerged as a critical topic in the discipline of the information era. AI and gadgets gaining knowledge of strategies can enhance the performance and accuracy of facts to get entry to strategies. Also, they can offer extra blessings, including advanced scalability, privacy, and protection. This paper examines the feasibility of incorporating AI for efficient information entry techniques. An in-depth review of present studies inside the place of AI for statistics gets entry is supplied, highlighting the challenges and possibilities related to its use. The paper also examines the practical factors of incorporating AI for information get entry to techniques and assesses the price, scalability, and complexity of such procedures. We concluded that incorporating AI for records access techniques is viable and might benefit information customers significantly. However, similar studies are wanted to decide the most incredible layout and implementation of such techniques.

关键词： Privacy Reviews Scalability Layout Process control Companies Complexity theory

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A Comprehensive Approach to Predict the Autism Spectrum Disorders using Deep Learning Mechanism

A Comprehensive Approach to Predict the Autism Spectrum Diso...

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International Conference on Smart Structures and Systems (ICSSS)

作者： Gayathri S Deepa A K.B. Aruna P. Vijayaraghavan V. Samuthira Pandi K. Pavithra Department of Computer Science and Engineering R.M.K. Engineering College Tamil Nadu India Department of Computer Science and Engineering Jeppiaar Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering S.A.Engineering College Chennai Tamil Nadu India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai Tamil Nadu India

Children often suffer from Autism Spectrum Disorder (ASD), a neurological disease. With stereotypical behavior and difficulties in social communication, ASD cannot be defined by chronological age alone. It is speculated that this condition results from malfunction in the brain caused by the interplay of several ecological, inherited and regulatory variables. More than one percent of the world's population has ASD, prompting policymakers, educators, and health care professionals to develop plans to combat the condition. Maximizing the potential of patients with autism demands a multidisciplinary approach. Therefore, it is crucial that various care models complement one another and do not cause the patient any unnecessary anxiety. Natural therapies have the potential to significantly enhance the long-term success of conventional treatments including psychotherapy, Occupational-Therapy (OT) and PT. According to the progression of symptoms, ASD can take on a number of different forms. However, there is still no agreed-upon gold standard therapy for ASD. If a systematic method is adopted, assessing a kid with suspected ASD is straightforward. There are substantial gains in interpersonal interaction, replication, as well as intellectual and adaptive performance following initial treatment. In this paper, a novel deep learning based prediction approach is designed called as Intelligent Learning Scheme to Predict Autism (ILSPA). This algorithm is cross-validated with the conventional learning approach called Deep Neural Network (DNN) to evaluate the performance and efficiency of the proposed scheme. The resulting scenario provides the proper proof to the efficiency of the proposed algorithm called ILSPA.

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Vehicle Prediction for BUS Identification Output from our Route Testing Real Time Algorithm

Vehicle Prediction for BUS Identification Output from our Ro...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： M. Misba R. Ramya Joel Dickson L. Sharmila J. Kavitha K. Udayakumar Department of Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Artificial Intelligence and Data Science St. Joseph's College of Engineering Chennai Bethlehem Institute of Engineering Karungal Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

Traffic generation in Indian cities is of a mixed nature, comprising over a dozen types of vehicles that can be broadly categorized into slow-moving and fast-moving vehicles. This study highlights the challenges associated with the inefficient operation of the existing bus system. GPS and mobile networks form integral components of the current system, enabling data storage and management using PostgreSQL. The system provides robust and reliable data to fulfill requests from other software applications. Operating on cloud platforms, it is compatible with all major web browsers. The proposed algorithm continuously tracks the real-time location of buses and their corresponding WMS (Web Map Service) location, alerting users through marker notifications. This paper also explores measures to evaluate the efficiency and effectiveness of bus services. Recognizing that an inadequate public transport system impedes economic growth and development, this study emphasizes the need to enhance the overall quality and accessibility of public transportation. The findings aim to improve the operational efficiency of bus services, contributing to a more effective and sustainable transport system.

关键词： Urban areas Software algorithms Memory Prediction algorithms Real-time systems Software Maintenance Software reliability Public transportation Testing

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FADE: Why Bad Descriptions Happen to Good Features

arXiv

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arXiv 2025年

作者： Puri, Bruno Jain, Aakriti Golimblevskaia, Elena Kahardipraja, Patrick Wiegand, Thomas Samek, Wojciech Lapuschkin, Sebastian Department of Artificial Intelligence Fraunhofer Heinrich Hertz Institute Berlin Germany Department of Electrical Engineering and Computer Science Technische Universität Berlin Berlin Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin Germany

Recent advances in mechanistic interpretability have highlighted the potential of automating interpretability pipelines in analyzing the latent representations within LLMs. While they may enhance our understanding of internal mechanisms, the field lacks standardized evaluation methods for assessing the validity of discovered features. We attempt to bridge this gap by introducing FADE: Feature Alignment to Description Evaluation, a scalable model-agnostic framework for evaluating feature-description alignment. FADE evaluates alignment across four key metrics — Clarity, Responsiveness, Purity, and Faithfulness — and systematically quantifies the causes for the misalignment of feature and their description. We apply FADE to analyze existing open-source feature descriptions, and assess key components of automated interpretability pipelines, aiming to enhance the quality of descriptions. Our findings highlight fundamental challenges in generating feature descriptions, particularly for SAEs as compared to MLP neurons, providing insights into the limitations and future directions of automated interpretability. We release FADE as an open-source package at: https://***/brunibrun/FADE. Copyright © 2025, The Authors. All rights reserved.

关键词： Open systems

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Kidney and Kidney Tumour Segmentation from 3D CT Scan using DeepLabv3+

Kidney and Kidney Tumour Segmentation from 3D CT Scan using ...

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IEEE Region 10 Symposium

作者： Taashna A. Jariwala Pranavi C. Mehta Mayuri A. Mehta Vivek C. Joshi Department of Artificial Intelligence and Data Science Sarvajanik College of Engineering and Technology Surat India Department of Computer Engineering Sarvajanik College of Engineering and Technology Surat India Department of Information Technology R. N. G. Patel Institute of Technology Bardoli India

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364873

Segmenting kidney and kidney tumour from CT scan is crucial in combating challenges in the early detection of kidney cancer. Several segmentation methods are available to segment kidney and kidney tumour from 3D CT scan. However, these methods pose several drawbacks, including dependency on pixel-wise classification, limited generalisation, and manual annotation requirements. Hence, this paper introduces a novel kidney and kidney tumour segmentation approach employing encoder-decoder-based architecture. The proposed segmentation approach is assessed against two encoder-decoder-based architectures, namely U-Net and DeepLabv3+. The proposed approach precisely identifies kidney and kidney tumour in a 3D CT scan. Its performance is analysed using the 2023 Kidney and Kidney Tumour Segmentation Challenge (KiTS23) dataset. The evaluation metrics such as dice coefficient and Intersection over Union (IoU) are used to assess the performance. Our results on the KiTS23 dataset show that DeepLabv3+ outperforms U-Net. Thus, the paper discusses the DeepLabv3+ approach in detail. DeepLabv3+ boasts an average improvement of 0.82% in dice coefficient, 1.60% in IoU, 39.28% in loss during training, and 0.94% in dice coefficient, 1.82% in IoU, 44.88% in loss during validation over U-Net.

关键词： Training Image segmentation Three-dimensional displays Annotations Computed tomography Supervised learning Robustness Kidney Tumors Biomedical imaging

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Apache Kafka on Big data Event Streaming for Enhanced data Flows

Apache Kafka on Big Data Event Streaming for Enhanced Data F...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： K Padmanaban T. R. Ganesh Babu K. Karthika Balachandra Pattanaik Dhanabhavithra K C. Srinivasan Department of Electronics and Communication Engineering Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Electronics and Communication Engineering KCG College of Technology Chennai Tamil Nadu India Department of Electrical and Computer Engineering Wollega University Nekemte Ethiopia Department of Artificial Intelligence and Data Science Agni College of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Apache Kafka's distributed architecture and message queuing capabilities offer significant improvements in real-time and batch data processing efficiency and reliability. This research aims to optimize Kafka setups, data partitioning, and Kafka Connect integration to create a robust and scalable data streaming infrastructure. The study focuses on enhancing data input, processing, and dissemination across systems and applications. By optimizing Kafka configurations and leveraging its capabilities, the research seeks to achieve significant improvements in data processing speed, real-time analytics, and scalable data pipelines. The evaluation of Kafka Event Stream Throughput Over Time and Latency Distribution Across Brokers demonstrates the system's performance and efficiency. The results indicate a throughput of 750-1340 events per hour and a latency distribution of 6-15 milliseconds. Additionally, Consumer Lag Over Time analysis reveals consistent performance with values ranging from 70-140. This research contributes to the advancement of big data processing by demonstrating the effectiveness of Apache Kafka in creating a robust and efficient data streaming infrastructure. The findings provide valuable insights for organizations seeking to optimize their data pipelines and leverage the power of real-time analytics.

关键词： System performance Pipelines Semantics Refining Distributed databases Transforms Big data data processing Throughput Real-time systems

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Enhanced Agricultural Methods and Sustainable Farming Through IoT and AI Technology

Enhanced Agricultural Methods and Sustainable Farming Throug...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Manivannan K. V Dankan Gowda BH. V.V.S.R.K.K. Pavan S. Aravindh C. Nithisha Rama chaithanya Tanguturi Department of Information Technology V.S.B. Engineering College Karur Tamilnadu India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka Department of ECE Bonam Venkata Chalamayya Institute of Technology and Science Autonomous Amalapuram Andhrapradesh India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Data Science School of Computing Mohan Babu University Sree Sainath Nagar Tirupati Andhra Pradesh India Department of Computer Science and Engineering PACE Institute of Technology and Sciences Ongole Andhra Pradesh India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

The combination of IoT and AI technologies in the future promises to disrupt agriculture positively while trying to solve problems affecting the sector through embracing technological advances. The following paper focuses on the development of an IoT and AI framework for enhancing practices in agriculture through real-time control and smart control. Wireless IoT sensors are placed in the farms to monitor important parameters of farms like moisture content, temperature, humidity etc and health of crops. Consequently, data is harvested to assist with accurate irrigation, climatic regulation, and early diagnosis of disease. From the experimental results observed it became clear that resource utilization was boosted by 25% of lesser water usage while crop yields were boosted by as much as 30%. It also early identifies crop diseases, hence cutting crop losses by 15%. Such is crucial to stress that the results obtained reveal the ability of the system to improve the sustainability and productivity of agriculture. Future work will comprise the fine-tuning of the AI model, enhancement of sensors, and their incorporation with other smart technologies besides adapting this strategy for various agricultural conditions, hence the importance of smart solutions in the promotion of eco-friendly farming.

关键词： Temperature sensors Productivity Irrigation Crops Internet of Things Artificial intelligence Sustainable development Water resources Monitoring Farming

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FairTP: A Prolonged Fairness Framework for Traffic Prediction 39

FairTP: A Prolonged Fairness Framework for Traffic Predictio...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xia, Jiangnan Yang, Yu Shen, Jiaxing Wang, Senzhang Cao, Jiannong School of Computer Science and Engineering Central South University China Centre for Learning Teaching and Technology The Education University of Hong Kong Hong Kong School of Data Science Lingnan University Hong Kong The Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)157735897X

Traffic prediction is pivotal in intelligent transportation systems. Existing works focus mainly on improving overall accuracy, overlooking a crucial problem of whether prediction results will lead to biased decisions by transportation authorities. In practice, the uneven deployment of traffic sensors in different urban areas produces imbalanced data, making the traffic prediction model fail in some urban areas and leading to unfair regional decision-making that eventually severely affects equity and quality of residents’ life. Existing fairness machine learning models struggle to maintain fair traffic prediction over prolonged periods. Although these models might achieve fairness at certain time slots, this static fairness will break down as traffic conditions change. To fill this research gap, we investigate prolonged fair traffic prediction, introducing two novel fairness metrics, i.e., region-based static fairness and sensor-based dynamic fairness, tailored to fairness fluctuations over time and across areas. An innovative prolonged fairness traffic prediction framework, namely FairTP, is then proposed. FairTP achieves prolonged fairness by alternating between "sacrifice" and "benefit" the prediction accuracy of each traffic sensor or area, ensuring that the number of these two actions are balanced over time. Specifically, FairTP incorporates a state identification module to discriminate whether the traffic sensors or areas are in a "sacrifice" or "benefit" state, thereby enabling prolonged fairness-aware traffic predictions. Additionally, we devise a state-guided balanced sampling strategy to select training examples to further enhance prediction fairness by mitigating the performance disparities among areas with uneven sensor distribution over time. Extensive experiments in two real-world datasets show that FairTP significantly improves prediction fairness without causing significant accuracy degradation. Copyright © 2025, Association for the Advancement of Ar

关键词： Traffic control

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RSZero-CSAT: Zero-Shot Scene Classification in Remote Sensing Imagery using a Cross Semantic Attribute-guided Transformer

RSZero-CSAT: Zero-Shot Scene Classification in Remote Sensin...

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International Joint Conference on Neural Networks (IJCNN)

作者： Damalla Rambabu Swetha G Rajeshreddy Datla Vishnu Chalavadi C Krishna Mohan Department of Computer Science and Engineering Indian Institute of Technology Hyderabad Dept. of Space Advanced Data Processing Research Institute (ADRIN) Secunderabad India Indian Institute of Technology Tirupati India

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Zero-shot learning (ZSL) based scene classification aims to recognize unseen classes by transferring semantic information from seen classes. The applicability of ZSL for scene classification in remote sensing images becomes challenging due to the complexity of scenes. Earlier attention-based methods are ineffective for extracting discriminative region-based features within a single image. This limitation hinders their ability to achieve transferability and accurately localize object attributes, which is essential for extracting discriminative region-based features. Hence, we propose a method for zero-shot scene classification in remote sensing images using a cross-semantic attribute-guided Transformer named RSZero-CSAT. Firstly, the semantic information is acquired using shared remote sensing semantic attributes to localize object attributes that characterize discriminative region features. Then, a Transformer in RSZero-CSAT is employed to localize object attributes within visual features accurately, enhancing the effectiveness of semantic information transfer in ZSL. Specifically, the RSZero-CSAT employs a semantic attribute → visual Transformer (SAVT) and a visual → semantic attribute Transformer (VSAT) components to extract visual features guided by semantic attributes and semantic attribute features guided by visual features, respectively. Further, SAVT and VSAT mutually learn and collaborate to obtain semantically enriched visual representations, leveraging prediction-level and feature-level semantic collaborative losses for capturing crucial semantic information. Finally, the semantically enriched visual representations obtained from SAVT and VSAT are combined to facilitate visual-semantic interactions in collaboration with class semantic vectors to classify ZSL. Our experimental results demonstrate the impact of RSZero-CSAT in improving the performance of unseen classes on four scene classification benchmark datasets in remote sensing images. The code is avai

关键词： Visualization Scene classification Zero-shot learning Semantics Collaboration Benchmark testing Feature extraction

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