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Image Processing and Capsule Networks (ICIPCN), International Conference on

作者： D. Vikram H Ajina S Gokilapriya M Malini Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore India Department of Artificial Intelligence and Data Science Rathinam Technical Campus Coimbatore India Department of Artificial Intelligence and Machine Learning Kalingarkarunanidhi Institute of Technology Coimbatore India Department of Information Technology Sri G.V.G Visalakshi College for Women Udumalpet India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Organizational efficiency is significantly influenced by automated attendance management systems, yet traditional methods often lack flexibility and reliability. This study proposes a novel approach to transform the Smart Attendance Management System (SAMS) by leveraging recent advances in Self-Supervised learning (SSL). The proposed method enhances accuracy and adaptability without explicit supervision by integrating SSL techniques at multiple stages, including feature extraction, anomaly detection, and data preparation. The proposed system learns meaningful representations from unlabeled attendance data through SSL-based data preprocessing, thereby improving the performance of downstream tasks. Feature extraction by utilizing SSL enables the proposed system to extract robust features enhancing both anomaly detection and attendance tracking. Anomaly detection models trained with SSL frameworks can effectively identify unusual attendance patterns. Additionally, SSL-based continuous learning ensures that the proposed system can adapt to the changing user behaviors and various situations. Supported by SSL, the proposed SAMS offers a more precise, efficient, and flexible solution for managing attendance, positioning it to meet the evolving demands of contemporary businesses across various industries.

关键词： Accuracy Soft sensors Self-supervised learning Transforms Medical services Feature extraction Real-time systems

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BLESSEMFLOOD21: ADVANCING FLOOD ANALYSIS WITH A HIGH-RESOLUTION GEOREFERENCED dataSET FOR HUMANITARIAN AID SUPPORT

arXiv

引用

arXiv 2024年

作者： Polushko, Vladyslav Jenal, Alexander Bongartz, Jens Weber, Immanuel Hatic, Damjan Rösch, Ronald März, Thomas Rauhut, Markus Weinmann, Andreas Image Processing Department Fraunhofer ITWM Kaiserslautern Germany Working Group Algorithms for Computer Vision Imaging and Data Analysis Hochschule Darmstadt Darmstadt Germany Center for Machine Learning and Sensor Technology Hochschule Koblenz Remagen Germany

Floods are an increasingly common global threat, causing emergencies and severe damage to infrastructure. During crises, organisations such as the World Food Programme use remotely sensed imagery, typically obtained through drones, for rapid situational analysis to plan life-saving actions. Computer Vision tools are needed to support task force experts on-site in the evaluation of the imagery to improve their efficiency and to allocate resources strategically. We introduce the BlessemFlood21 dataset to stimulate research on efficient flood detection tools. The imagery was acquired during the 2021 Erftstadt-Blessem flooding event and consists of high-resolution and georeferenced RGB-NIR images. In the resulting RGB dataset, the images are supplemented with detailed water masks, obtained via a semi-supervised human-in-the-loop technique, where in particular the NIR information is leveraged to classify pixels as either water or non-water. We evaluate our dataset by training and testing established Deep learning models for semantic segmentation. With BlessemFlood21 we provide labeled high-resolution RGB data and a baseline for further development of algorithmic solutions tailored to flood detection in RGB imagery. © 2024, CC BY-NC-ND.

关键词： Floods

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The Future of Driving: A Review on the Combination of Artificial Intelligence and Autonomous Vehicles

The Future of Driving: A Review on the Combination of Artifi...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Nikita Zade Mayur Gawande Prateek Verma Swapnil Gundewar Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The convergence of Artificial Intelligence (AI) and Autonomous Vehicles (AV) are poised to revolutionize the automotive industry, offering safer, more efficient, and sustainable transportation solutions. This review article explores the current state of AVs, role of AI in AV, challenges and hurdles, Real world applications & development, its critical technological components, and their impact on society. This paper presents a wide array of applications of AI in AV, elaborating on the way complex sensor fusion, computer vision, and Machine learning (ML) algorithms makes AVs perceive their surroundings, form critical opinions, and carry out accurate motions. AI is making real time interpretations of great quantities of data which are paving the way for safer and more effective navigation under a diversity of traffic circumstances, from sensor fusion to decision-making algorithms. The next section of this paper elaborates on the role of AI in the field of AV in perceiving, planning, and navigating through real world environment, while also talking about issues of integration and ethics. This paper further identifies its social, economic, and environmental consequences, resulting from mass use of AV. This article discusses the latest developments and opportunities in the regulatory environment, gives an overview of how AV define transportation in the near future. This paper also explores the levels of autonomy of AVs as defined by the Society of Automotive Engineers (SAE) and defines the process from driver assistance to full automation. It examines the ethical and technical considerations arising at each instant and emphasizes the key role AI continues to play in providing more autonomy while preserving security and dependability.

关键词： Ethics Machine learning algorithms Reviews Navigation Decision making Sensor fusion Safety Artificial intelligence Socioeconomics Automotive engineering

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CONVERGENCE ANALYSIS OF PROBABILITY FLOW ODE FOR SCORE-BASED GENERATIVE MODELS

arXiv

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arXiv 2024年

作者： Zhengyu Huang, Daniel Huang, Jiaoyang Lin, Zhengjiang Beijing International Center for Mathematical Research Center for Machine Learning Research Peking University Beijing China Department of Statistics and Data Science University of Pennsylvania PhiladelphiaPA United States Department of Mathematics Massachusetts Institute of Technology CambridgeMA United States

Score-based generative models have emerged as a powerful approach for sampling high-dimensional probability distributions. Despite their effectiveness, their theoretical underpinnings remain relatively underdeveloped. In this work, we study the convergence properties of deterministic samplers based on probability flow ODEs from both theoretical and numerical perspectives. Assuming access to L2-accurate estimates of the score function, we prove the total variation between the target and the generated data distributions can be bounded above by O(d3/4δ1/2) in the continuous time level, where d denotes the data dimension and δ represents the L2-score matching error. For practical implementations using a p-th order Runge-Kutta integrator with step size h, we establish error bounds of O(d3/4δ1/2 + d· (dh)p) at the discrete level. Finally, we present numerical studies on problems up to 128 dimensions to verify our theory. Copyright © 2024, The Authors. All rights reserved.

关键词： Distribution functions

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Tropical Cyclone Detection and Tracking Using YOLOv8 Algorithm

Tropical Cyclone Detection and Tracking Using YOLOv8 Algorit...

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Cognitive, Green and Ubiquitous Computing (IC-CGU), International Conference on

作者： Saroja Kumar Rout Kottu Santosh Kumar Ruchismita Sahu Shekharesh Barik Samarendra Pradhan Department of Information Technology Vardhaman College of Engineering (Autonomous) Hyderabad India Department of Artificial Intelligence and Machine Learning Vardhaman College of Engineering (Autonomous) Hyderabad India Department of Computer Science & Engineering Templecity Institute of Technology and Engineering Bhubaneswar India Department of Computer Science & Engineering DRIEMS University Cuttack India Data scientist Jayapur Cuttack India

ISBN: (数字)9798350361780

ISBN: (纸本)9798350361797

Coastal areas around the world face a great threat from tropical cyclones, which makes timely and accurate identification essential for efficient disaster response. An enhanced method for detecting tropical cyclones is proposed in this work using the YOLOv8 (You Only Look Once version 8) object identification algorithm. Using the real-time object recognition capabilities of YOLOv8, the methodology is designed to tackle the challenges associated with finding cyclonic structures in satellite data. To detect crucial features typical of tropical cyclones—like cloud formations, circular symmetry, and dynamic atmospheric fluctuations—the YOLOv8 architecture is improved. To ensure its adaptability to different climatic circumstances, the model is trained using a varied dataset of labeled satellite photos. The YOLOv8-based methodology is superior to existing methods in detecting tropical cyclones with accuracy and efficiency almost in real-time, as demonstrated by the experimental results. Because of the algorithm's excellent precision, recall, and computing economy, early warning systems and catastrophe preparedness can benefit greatly from it. This research contributes to the ongoing effort to develop detection approaches for increased resilience in vulnerable coastal areas, while also utilizing YOLOv8's capabilities to address the global threat posed by tropical cyclones. This study can help clarify the necessity of improving models and conducting better observations of Tropical Cyclones.

关键词： Deep learning YOLO Satellites Tropical cyclones Atmospheric modeling Sea measurements Computer architecture

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Functional and Non-Functional Requirement (FR and NFR) Formation Based on Requirement Elicitation Using Text Semantics in IdVar4CL Artifact

Functional and Non-Functional Requirement (FR and NFR) Forma...

引用

International Conference on Signal Processing and Communication (ICSC)

作者： Nayla Fatimatuzzahra Yudi Priyadi Rio Nurtantyana Department of Information Technology Telkom University Bandung Indonesia Department of Software Engineering Telkom University Bandung Indonesia Center of Excellence Artificial Intelligence for Learning and Optimization & Research Center for Data and Information Sciences Telkom University & National Research and Innovation Agency (BRIN) Bandung Indonesia

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

Elicitations are mandatory in establishing both Functional Requirements (FR) and Non-Functional Requirements (NFR). One of the challenges in software engineering is to ensure that Requirement Elicitation (RE) is accurately converted to well-structured FRs and NFRs in Software Requirements Specification (SRS) documentation. However, traditional approaches for requirements gathering often face challenges such as incomplete information, evolving requirements, and lack of stakeholder involvement. To address this problem, we adopted agile methodologies in software development. Hence, this study proposed to form FR and NFR based on RE through a semantic text process. The results showed that we have been successful in establishing FR and NFR on the RE process using text semantics in the IdVar4CL artifact, which included 29 documents (d1-d29), consisting of 12 RE documents, 10 FR documents and 7 NFR documents. In detail, the list of Requirement Specification tabulations in Subject, modals, Verb, Object format. Semantic similarity through comparison of the elicitation documentation with the SRS documentation results in a difference of 0.09 as a reference to subsequent SRS artifact formation improvements. Hence, this approach improves the clarity and alignment of requirements using agile development.

关键词： Semantics Documentation Signal processing Software Stakeholders Faces Software engineering Software development management

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SPEAR: Design and Implementation of an Advanced Virtual Assistant

SPEAR: Design and Implementation of an Advanced Virtual Assi...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Garima Jain Amita Shukla Nitesh Kumar Bairwa Anamika Chaudhary Ashish Patel Ankush Jain Department of Computer Science and Buisness Systems Noida Institute of Engineering and Technology Greater Noida India Department of Artificial Intelligence Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Greater Noida India Department of Artificial Intelligence and Machine Learning Dronacharya Group of Institutions Greater Noida India Department of Computer Science and Enginnering Netaji Subhash University of Technology Delhi India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

This research presents the development and evaluation of SPEAR, an advanced voice-activated personal desktop assistant designed to address challenges in existing virtual assistant technology, such as limited language support, low recognition accuracy, and inadequate task automation. Despite recent advancements in Natural Language Processing (NLP) and machine learning, implementation in current systems remains subpar. SPEAR achieves an 88% overall command execution success rate, with a speech recognition accuracy of 92% and an average response time of 1.8 seconds, demonstrating efficiency in real-time interactions. Developed using Python and machine learning libraries, SPEAR supports 15 voice commands across multiple platforms, excelling in note creation (95% success), web searching (94%), and information retrieval (92%). Its natural language understanding score of 0.86 and context retention rate of 75% highlight its conversational capabilities. While the assistant performs well in messaging functionality (85% success) and currently supports three languages, expanding its multilingual capabilities is essential for broader user engagement. Metrics indicate a satisfaction score of 8.2/10 and an average of 12 daily interactions per user, showcasing its practical utility. Additionally, the system's text-to-speech quality (8.5/10) and error recovery rate (82 %) affirm its robustness. These findings contribute valuable insights for future research, focusing on enhancing multilingual support, broadening task categories, and improving context retention, ultimately positioning SPEAR to rival established industry leaders by offering a comprehensive suite of services.

关键词： Accuracy Personal voice assistants Machine learning Information retrieval Natural language processing Real-time systems Robustness Text to speech Time factors Web search

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Giant Trevally Optimizer (GTO): Enhancing HVDC Transmission Capacity with Optimized Fault Current Limiters and HVDC Circuit Breaker Parameters

Giant Trevally Optimizer (GTO): Enhancing HVDC Transmission ...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： M. Siva Ramkumar Saranya. N Gokul Gopan Rahmath Ulla Baig Josha Daniel S Babu M Department of ECE SNS College of Technology India Department of Artificial Intelligence and Data Science Karpagam College of Engineering India Department of Mechanical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India Department of Artificial Intelligence and Machine Learning Cambridge Institute of Technology K R Puram Bangalore Department of EEE Hindustan College of Engineering and Technology India Master of Engineering in Embedded Systems Hindusthan College of Engineering and Technology India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The multi-terminal HVDC system relies heavily on circuit breakers (CBs) and fault current limiters (FCLs) for protection and performance reliability. This research describes a new Giant Trevally optimizer-based strategy for improving the performance of 4-terminal HVDC systems by optimizing crucial circuit breakers and fault current limiters. The proposed method uses time domain estimation to correctly capture fault dynamics, such as traveling waves and reflection coefficients and finds the worst-case fault locations. The multi-objective optimization seeks to optimize the overcurrent, overvoltage handling, fault clearance time, and energy absorption in arresters. This technology provides an effective, dependable protection solution that reduces fault recovery time and increases system stability. The output pulse of the HVDC system shows very low THD results 1.98% for the minimum load condition, 0.87% for median load, and 0.04% for peak load conditions. The results established the simulation of the model in a MATLAB environment.

关键词： HVDC transmission Circuit breakers Stability analysis Circuit faults Fault current limiters Time-domain analysis Protection Voltage control Integrated circuit reliability Optimization

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The Age and Gender Detection using Deep Convolution Neural Network

The Age and Gender Detection using Deep Convolution Neural N...

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Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Shweta Chauhan Rahul Kumar Bibek Kumar Department of artificial intelligence and Machine Learning Madhav Institute of science and technology Gwalior Madhya Pradesh India ECE Department GLA University Mathura Uttar Pradesh India School of Data Science Symbiosis University of Applied Sciences Indore Madhya Pradesh India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Automatic age and gender classification has become important researcher since the rise of social platforms and social media. The main advancements in face recognition are the ability to determine a person's age and gender. This paper makes a significant contribution by providing a survey of different face recognition methods for determining age and gender. A deep neural network (DNN) known as a convolutional neural network is frequently used for natural language processing and image recognition and processing. The proposed method significantly enhances three elements: face alignment, face recognition, and face recognition accuracy. All weight vectors of people in the same age group within a given database are averaged together. To predict gender and age, picture classification and regression models have been discussed in this paper respectively. Although training the proposed network to make accurate predictions takes a lot of time, it significantly improves age and gender categorization precision. From the experimental results we compared and analyzed the work to the most recent UTK-Face benchmark for estimating age and gender.

关键词： Training Surveys Accuracy Image recognition Social networking (online) Face recognition Predictive models Vectors Natural language processing Convolutional neural networks

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Natural Language Processing in Virtual Assistants Current Approaches and Challenges

Natural Language Processing in Virtual Assistants Current Ap...

引用

2024 International Conference on Science, technology, Engineering and Management, ICSTEM 2024

作者： Solaimalai, Gautam Shanthi, J. Maria Kumar, Sampath S. Khabiya, Priyanka Geetha, K. Talele, Gokul U.S. Bank GA30041 United States Department of Artificial Intelligence and Machine Learning J. B. Institute of Engineering and Technology Telangana Hyderabad500075 India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore641202 India Department of Computer Science Engineering Mandsaur University Madhya Pradesh Mandsaur458002 India Department of Computer Science G.T.N. Arts College Tamil Nadu Dindigul624005 India Department of Data Science Organization name Accenture Maharashtra Pune411013 India

ISBN: (纸本)9798350376913

The proliferation of virtual sidekicks in colorful disciplines has prodded a swell of interest in advancing Natural Language Processing(NLP) ways to enhance their effectiveness. This paper provides a comprehensive review of the current approaches and challenges encountered in integrating NLP into virtual sidekicks. It begins by outlining the foundational generalities of NLP and its vital part in enabling mortal- suchlike relations with virtual sidekicks. latterly, it delves into a discussion of the different methodologies employed in NLP for understanding and generating mortal language, ranging from rule-grounded systems to deep literacy models. likewise, the paper highlights the crucial challenges such as nebulosity resolution, environment understanding, and handling different verbal variations that stymie the flawless functioning of virtual sidekicks. By synthesizing perceptivity from recent exploration trials, this paper offers precious perspectives on the state-of-the-art NLP ways and directions for unborn exploration to overcome the challenges and propel the development of further intelligent and intuitive virtual sidekicks. © 2024 IEEE.

关键词： Deep learning

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