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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Chaudhary, Pranav Kumar Yalamati, Sreedhar Palakurti, Naga Ramesh Alam, Naved Kolasani, Saydulu Whig, Pawan Amazon United States Solutions Architect Celer Inc. Systems CA United States Department of Computer Science and Engineering Jamia Hamdard New Delhi India Enterprise Digital Operations CA United States Threws VIPS-TC New Delhi India Solutions Architect United States

ISBN: (纸本)9798350361537

This research presents a pioneering approach to combat child cyberbullying utilizing generative artificial intelligence (AI) techniques. Our system achieved an impressive detection accuracy of 92.5%, with a precision of 89% and recall of 95%, surpassing traditional methods by 15% in accuracy and 10% in recall. Additionally, the system exhibited rapid response times, with an average of 0.5 seconds for flagging and classifying cyberbullying incidents. Proactive prevention strategies, including the generation of counter-narratives and positive interventions, resulted in a 30% reduction in the escalation of cyberbullying incidents and a 25% increase in the utilization of support resources by affected individuals. Furthermore, the system demonstrated scalability and adaptability, maintaining consistent performance across diverse datasets and showing a 5% increase in detection accuracy over a six-month period. These findings underscore the potential of generative AI in creating safer online environments for children by effectively detecting and preventing cyberbullying behaviors. © 2024 IEEE.

关键词： Ambient intelligence

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An Investigation of Smart Detection for Small Lung Tumor with Tumor Pattern Recognition Algorithm

An Investigation of Smart Detection for Small Lung Tumor wit...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Hariharan, G. Prasanth, P. Devarani, P. Arthi Sajana, T. Indhumathi, C. Kumar, Ashok Malla Reddy University Deportment of Artificial Intelligence and Machine Learning Telangana Hyderabad500100 India Vel Tech Multi Tech Dr. Rangarajan Dr.Sakunthala Engineering College Department of Information Technology Tamil Nadu Chennai600062 India R. M K College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Thiruvallur601206 India KoneruLakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh Vaddeswaram522502 India R.M. K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavaraipettai601206 India BanasthaliVidyapith Department of Computer Science Rajasthan304022 India

ISBN: (纸本)9798350334357

The Small-cell lung tumor is the prime public concern, resulting in increased mortality. Various therapeutic approaches have made progress in the handling of small-cell lung tumor. It's considered the backbone of cytotoxic chemotherapy treatment. However, clinical research shows that because small-cell lung tumor is chemically approachable, high response rates among small-cell lung tumorinfecters are generally not translated into cure. Therefore, it is high time to make progress in small-cell lung tumor research because every success in clinical trials is fought, and every failure is a lesson. A recent study of small-cell lung tumor has improved the prognosis of patients. Some notable research areas have provided new options for patients by conducting multiple clinical trials. In this paper smart edge detection based small lung tumor identification method was proposed. This model operates with the pattern recognition algorithm model. This algorithm smartly finds the edges of the tumor cells and provides the edge results. This will helpful for the doctors to identify the tumor part exactly. © 2023 IEEE.

关键词： Tumors

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Analog, Programmable Switched Capacitor FIR Filter Based on Rotator Architecture Implemented in CMOS Technology

Analog, Programmable Switched Capacitor FIR Filter Based on ...

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Signal Processing Algorithms, Architectures, Arrangements and Applications (SPA)

作者： Paweł Pawłowski Rafał Długosz Marek Radkowski Mateusz Wątły Adam Dąbrowski Faculty of Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Division of Signal Processing and Electronic Systems Poznań University of Technology Poznań Poland Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz University of Science and Technology Bydgoszcz Poland

The paper presents project and its verification of a prototype integrated circuit containing an analog, programmable finite impulse response (FIR) filter, implemented in CMOS 350 nm technology. The structure of the filter is based on the switched capacitor technique. In circuits of this type, one of main challenges is an efficient implementation of filter coefficients, which result from several factors described in this work. When implementing such filters as programmable circuits, the values of their coefficients have to be limited to a selected range, i.e. a given resolution in bits. In the implemented prototype filter, the filter coefficients are represented by 6 bits in sign-magnitude notation, so they can take 63 different values only. In such filters, it is not possible to directly implement any frequency response of the filter. Each time, it is necessary to properly round the theoretical values of the coefficients so that they fit into the available range of discrete values resulting from the implementation. The authors of the work designed an algorithm that allows such matching. The paper also presents results of measurements of the prototype chip.

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A Distributed Algorithm for Identifying Strongly Connected Components on Incremental Graphs

A Distributed Algorithm for Identifying Strongly Connected C...

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International Symposium on computer Architecture and High Performance Computing (SBAC-PAD)

作者： S. Srinivasan A. Khanda A. Pandey S. K. Das S. Bhowmick B. Norris Department of Computer Science University of Oregon Eugene OR USA Department of Computer Science and Engineering University of North Texas Denton TX USA Department of Computer Science Missouri University of Science and Technology Rolla MO USA Department of Management Information Systems Bowie State University MD

Incremental graphs that change over time capture the changing relationships of different entities. Given that many real-world networks are extremely large, it is often necessary to partition the network over many distributed systems and solve a complex graph problem over the partitioned network. This paper presents a distributed algorithm for identifying strongly connected components (SCC) on incremental graphs. We propose a two-phase asynchronous algorithm that involves storing the intermediate results between each iteration of dynamic updates in a novel meta-graph storage format for efficient recomputation of the SCC for successive iterations. To the best of our knowledge, this is the first attempt at identifying SCC for incremental graphs across distributed compute nodes. Our experimental analysis on real and synthesized graphs shows up to 2.8x performance improvement over the state-of-the-art by reducing the overall memory utilized and improving the communication bandwidth.

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Robotics in Healthcare: Artificial Intelligence-Driven Application for Medical Diagnosis and Treatment

Robotics in Healthcare: Artificial Intelligence-Driven Appli...

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Innovative Computing, Intelligent Communication and Smart Electrical systems (ICSES), International Conference on

作者： Dhanya D A. Devendran S. Balaji Vaishali M R. Sujatha K.V. Sreelekha Department of Artificial Intelligence and Data Science Mar Ephraem College of Engineering and Technology Kanyakumari Tamil Nadu India School of Business Woxsen University Hyderabad Telangana India Department of Information Technology Sri Manakula Vinayagar Engineering College Puducherry India Deportmet of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Science SubbalakshmiLakshmipathy College of Science Madurai Tamil Nadu India Department of Computer Science and Engineering Hindusthan College of Engineering and Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The rapid development of deep learning methods presents a potentially game-changing opportunity in the realm of education, particularly in the promotion of student involvement and the comprehension of the subject matter. To exemplify learning gestures and resolve educational challenges, this exploratory study investigates the operation of deep learning algorithms to determine the interests of students. Our deep learning model can provide direct predictions on the areas of interest for individual students by analyzing enormous volumes of educational data. These data include pupil relations, performance standards, and behavioral patterns of students. By aligning instructional tactics with the preferences of students, this strategy not only makes it easier for students to become accustomed to newly presented educational material but also encourages active learning. The research reveals that deep learninghelps capture the intricacies of student engagement. It also provides preceptors with valuable insights that may be used to cultivate a learning landscape that is more engaging and investigative. The results of our research highlight the possibility that deep learning will be used to change educational procedures to make them more adaptable and sensitive to the various needs of students. In this paper, the practice of using deep learning for interest identification in education is discussed, along with its methodology, perpetrators, and counteraccusations

关键词： Deep learning Education Surgery Predictive models Robot sensing systems Prediction algorithms Real-time systems Pupils Medical diagnostic imaging Standards

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On Uniform Observability of Linear Time-varying systems with respect to Subspaces and Its Applications

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IEEE Transactions on Automatic control 2025年

作者： Liu, Tao Huang, Jie Southern University of Science and Technology Shenzhen Key Laboratory of Control Theory and Intelligent Systems Shenzhen518055 China The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Shatin Hong Kong

Recently, the notion of uniform observability with respect to a subspace was proposed in [21] and was utilized to solve the synchronization problem for single-integrator multi-agent systems with time-varying matrix-weighted couplings. This paper further explores the concept of uniform observability of a linear time-varying system with respect to a subspace. We will first establish the invariance property of uniform observability with respect to some invariant subspace under time-varying output feedback control. Then, based on this invariance property, we will establish an exponential convergence result with respect to an invariant subspace for a class of linear switched systems. Leveraging this exponential convergence result, we will solve the exponential synchronization problem for general linear multi-agent systems over jointly connected switching networks. It is noted that the exponential synchronization problem was also studied recently by some more sophisticated methods. Compared with these methods, our method is much simpler. Moreover, our method leads to an explicit characterization of the exponential synchronization rate. © 1963-2012 IEEE.

关键词： Linear time-invariant system

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ANSR-DT: An Adaptive Neuro-Symbolic Learning and Reasoning Framework for Digital Twins

arXiv

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arXiv 2025年

作者： Hakim, Safayat Bin Adil, Muhammad Velasquez, Alvaro Song, Houbing Herbert The Department of Information Systems University of Maryland Baltimore County BaltimoreMD21250 United States The Department of Computer Science and Engineering University at Buffalo BuffaloNY14260 United States The Department of Computer Science University of Colorado Boulder BoulderCO80309 United States

In this paper, we propose an Adaptive Neuro-Symbolic Learning Framework for digital twin technology called "ANSR-DT." Our approach combines pattern recognition algorithms with reinforcement learning and symbolic reasoning to enable real-time learning and adaptive intelligence. This integration enhances the understanding of the environment and promotes continuous learning, leading to better and more effective decision-making in real-time for applications that require human-machine collaboration. We evaluated the ANSR-DT framework for its ability to learn and adapt to dynamic patterns, observing significant improvements in decision accuracy, reliability, and interpretability when compared to existing state-of-the-art methods. However, challenges still exist in extracting and integrating symbolic rules in complex environments, which limits the full potential of our framework in heterogeneous settings. Moreover, our ongoing research aims to address this issue in the future by ensuring seamless integration of neural models at large. In addition, our open-source implementation promotes reproducibility and encourages future research to build on our foundational work. © 2025, CC BY.

关键词： Adversarial machine learning

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Estimating Remaining Useful Life of Cutting Tools in Machining Using an Extended Kalman Filter

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IFAC-PapersOnLine 2024年第19期58卷 1264-1269页

作者： Qian Yang Debasish Mishra Krishna R. Pattipati George M. Bollas Department of Electrical and Computer Engineering University of Connecticut Storrs CT 06269 USA Data Science & Information Systems Krea University 5655 Central Expressway Sri City 517646 India Pratt & Whitney Institute of Advanced Systems Engineering University of Connecticut Storrs CT 06269 USA

Monitoring the condition of the cutting tool and forecasting the evolution of its wear during machining are vital to ensure workpiece quality and the safety of machine elements. The accurate prediction of tool wear is crucial to advance predictive maintenance in machining. This article presents a methodology to estimate the remaining useful life (RUL) of cutting tools. The methodology involves extracting features from vibration signals using discrete wavelet transform (DWT) and computing the concomitant indicators of tool health by applying distance metrics to the features. These indicators have a high correlation with tool wear measurements. The RUL is estimated by fusing these indicators in a support vector regression (SVR) algorithm. The SVR outputs then serve as inputs to an extended Kalman filter (EKF) that employs a rectified linear activation unit (ReLU) function for state evolution, enabling real-time estimation of the RUL. The proposed methodology is demonstrated on the IEEE PHM 2010 dataset, showcasing its reliability and effectiveness in accurately estimating the RUL for cutting tools.

关键词： Remaining useful life (RUL) Health indicator Distance metrics Support vector regression Extended Kalman filter (EKF) Cutting tool Machining

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Intelligent Document Finding using Optical Character Recognition and Tagging 1

Intelligent Document Finding using Optical Character Recogni...

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1st IEEE International Conference on Automation, Computing and Renewable systems, ICACRS 2022

作者： Abbas, A. Mohamed Hameed, M. Syed Shahul Balakrishnan, S. Anandh, K.S. University of Technology and Applied Sciences - SUR PO BOX: 484 PC 411 Oman Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Tamilnadu Coimbatore India Department of Civil Engineering College of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur India

ISBN: (纸本)9781665460842

In the era of digitalization, the assortment and exploration of great volumes of documents is becoming progressively significant for enterprises to increase their productions and practices. Optical Character Recognition (OCR) is a procedure of identifying text in scanned (image-based) documents. This paper aims to deliver seamless searching of documents in file systems using Optical Character Recognition (OCR) and Natural Language Processing (NLP). Our paper includes the following phases: "Text Identification (in terms of text files), Image Capturing, Image Enhancement, Image Identification, OCR, Data Extraction and Quality Assurance". In case of text files, the data extraction is done in the first phase itself. The document management system "processes both structured document images (ones which have a standard format) and unstructured document images" (ones which do not have a standard format). In the tagging phase, the document is divided into segments and the tags for each segment are generated using Natural Language Processing. © 2022 IEEE

关键词： Optical character recognition

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Small-UAV GPS Spoofing Attack Detection and Multilabel Classification

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Security and Privacy 2025年第3期8卷

作者： Alsubai, Shtwai Sampedro, Gabriel Avelino Almadhor, Ahmad Karovic, Vincent Hejaili, Abdullah Al Abbas, Sidra College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Computer Science University of the Philippines Diliman Quezon City Philippines Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakaka Saudi Arabia Department of Information Management and Business Systems Faculty of Management Comenius University in Bratislava Bratislava Slovakia Faculty of Computers & Information Technology Computer Science Department University of Tabuk Tabuk Saudi Arabia Department of Computer Science COMSATS University Islamabad Pakistan

Small unmanned aerial vehicles (UAVs) rely heavily on global positioning systems (GPS) for navigation. Nevertheless, it is possible to launch GPS spoofing attacks, which makes the UAVs' ability to track themselves inexplicable. To solve this issue, this study proposed an ensemble stacking method to identify GPS signal spoofing in small UAVs. It contributes to the dataset extraction that contains information from real GPS signals collected from various locations to simulate an automated and stationary autonomous vehicle using an accessible software radio peripheral unit established as a GPS receiver and 13 features extracted from eight parallel mediums. We use a grid search approach to identify and fine-tune hyperparameters to improve the detection rate. The results show that the proposed approach performed well in traditional machine learning approaches and accurately predicts spoofing attacks with an accuracy of 98%. The results suggested that the proposed ensemble stacking performs better than alternative methods. The findings show that the proposed approach outperforms spoofing small-UAV GPS signal diagnosis techniques. © 2025 John Wiley & Sons Ltd.

关键词： Global positioning system

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