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Routability-driven Ordered Escape Routing Method based on Neural Network

Routability-driven Ordered Escape Routing Method based on Ne...

引用

2025 International Workshop on Advanced Imaging technology, IWAIT 2025

作者： Yeh, Hung Liang Cheng, Dun Wei Department of Computer Science and Information Engineering National Yunlin University of Science & Technology Yunlin Taiwan

ISBN: (数字)9781510688131

ISBN: (纸本)9781510688124

This study presents an innovative approach that utilizes neural network-based techniques to address the Ordered Escape Routing (OER) *** OER problem holds a crucial position in integrated circuit design, requiring multiple pins to be connected to the boundary of a chip in a specific order while ensuring that the routing paths do not ***, the total wire length must be minimized during the *** tackle this challenging problem, we propose a novel routability-driven method that leverages neural networks (NN) to predict feasible routing paths that satisfy the required *** approach not only ensures non-crossing paths but also strictly adheres to the specified pin connection order, while further minimizing the total wire length under these *** core of this technique lies in the efficient predictive capabilities of neural networks. © 2024 SPIE.

关键词： Convolutional neural networks

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Advances in Remote Sensing Technologies for Ecological Monitoring and Applications in Carbon Stock Estimation 3

Advances in Remote Sensing Technologies for Ecological Monit...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sadhasivam, Shankaripriya Gayathri, G. Poongothai, P. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of technology Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

This review explores recent advancements that are made in remote sensing, enabling detailed analysis of our ecosystem through high resolution and multispectral imaging. The recent advancements in remote sensing includes developments useful for generation of synthetic images using GAN such as StyleGAN3 and for estimation of carbon stocks in forest using OBIA. Ecotones like mangroves and saltmarsh are crucial for carbon sequestration but mapping them accurately was challenging due to spectral similarity and seasonal variations. Focusing on coastal protection by this review examines the mapping methodologies such as random forest classification, OBIA and FID for habitat distinction, canopy area assessment and analysis. Through these technologies data accuracy can be enhanced for better monitoring. This review concludes with a discussion of AI-powered image synthesis and classification. © 2025 IEEE.

关键词： Carbon sequestration

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A Comparative Study of IDS-Based Deep Learning Models for IoT Network

A Comparative Study of IDS-Based Deep Learning Models for Io...

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2023 International Conference on Advances in Artificial Intelligence and Applications, AAIA 2023

作者： Shaker, Bassam Noori Al-Musawi, Bahaa Qasim Hassan, Mohammed Falih Faculty of Computer Science and Information Technology University of Al-Qadisiyah Iraq Faculty of Engineering University of Kufa Iraq

ISBN: (纸本)9798400708268

The proliferation of connected devices within Internet of Things (IoT) networks has underscored the critical necessity of developing efficient Intrusion Detection Systems (IDS) that can adeptly identify and counteract threats. Deep learning algorithms are better than classical machine learning at automatically learning patterns and representations from raw data, enabling them to detect complex attacks. This research aims to conduct a comparative study of IDSs implemented through three deep learning models: Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Recurrent Neural Networks (RNN). The study employs three datasets (NF-UNSW-NB15, NF-BoT-IoT, and NF-ToN-IoT) to assess model performance in both binary and multiclass classification scenarios. The findings indicate that for binary traffic classification, DNN outperforms the other two models with an accuracy bout (98%) for all datasets. In the context of multiclass traffic classification, the DNN model surpasses the performance of the other model except for NF-ToN-IoT a CNN model score (69.08%). © 2023 Copyright held by the owner/author(s).

关键词： Internet of things

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Liver disease Prediction using Ensemble Learning 3

Liver disease Prediction using Ensemble Learning

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Pilankar, Atharva Shyamala, L. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

The increasing prevalence of liver disease necessitates efficient identification methods to alleviate the diagnostic burden on healthcare providers. Machine learning offers a promising solution by evaluating vital parameters to predict liver disease, thereby reducing the risk of incorrect diagnoses and subsequent financial burdens on patients. Ensuring that predictive models generalize well across diverse populations and demographics is crucial for their wide applicability. Standardized evaluation metrics for liver disease prediction models are essential for unbiased comparisons and benchmarking. Demonstrating the superiority of new models over existing clinical methods is key to their adoption in clinical practice. This research explores the use of ensemble learning techniques to enhance the accuracy of liver disease predictions. By combining various supervised machine learning models, including Support Vector Machines (SVM), logistic regression, gradient boosting, decision trees, random forests, and artificial neural networks, the predictive outcomes have been improved. Each model undergoes extensive hyperparameter tuning to identify the optimal parameters for the given dataset. Additionally, feature selection techniques are employed to determine the most significant features contributing to the prediction. The study implements both stacking and bagging classification methodologies, achieving an accuracy rate of 79%. This hybrid approach demonstrates significant potential in improving the reliability and accuracy of liver disease predictions, offering a robust tool for early diagnosis and management of liver disease in diverse patient populations. © 2024 IEEE.

关键词： Support vector machines

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Articulation Node Based Mobile Node Routing Protocol in a Hybrid IoT Network 2

Articulation Node Based Mobile Node Routing Protocol in a Hy...

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2nd IEEE International Conference on Recent Advances in Information technology for Sustainable Development, ICRAIS 2024

作者： Leelavathi, R. Vidya, A. Vivekananda Institute of Technology Computer Science and Engineering Department Bengaluru India

ISBN: (数字)9798350354461

ISBN: (纸本)9798350354461

In static IoT networks, fixed sensor nodes can create a bottleneck at central hubs, leading to congestion and rapid energy depletion, which may result in network partitioning or failure. Existing routing protocols, such as RPL, are not well-equipped to handle these issues effectively. To overcome these challenges, we propose the Articulation Node-Based Mobile Node Routing Protocol for LLNs (AMRPL), designed to address the limitations of traditional static networks. AMRPL dynamically identifies articulation nodes, which are critical for maintaining connectivity, and instructs mobile nodes to balance the load by moving towards these points. This approach mitigates the congestion and energy depletion issues found in traditional methods. Simulation results, conducted in the Cooja environment, demonstrate that AMRPL outperforms RPL by improving Packet Delivery Ratio (PDR), reducing End-to-End delay, and lowering energy consumption through efficient load distribution by mobile nodes. These improvements underscore the protocol's capability to enhance the overall performance and reliability of hybrid IoT networks. © 2024 IEEE.

关键词： Energy efficiency

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Software Reliability Growth Testing Effort Function Model Dependent on Machine Learning and Neural Network Algorithm

Software Reliability Growth Testing Effort Function Model De...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Farooq, Refath Chishti, Mohammad Ahsan National Institute of Technology Department of Computer Science Engineering Srinagar India

ISBN: (纸本)9798350372229

The quantification of dependency stands as a crucial element in programming quality. The Software Reliability Growth Model (SRGM) is a commonly utilized tool for evaluating reliability across diverse and challenging scenarios. However, the accuracy of traditional time-sensitive SRGMs may be compromised when test execution extends over a prolonged period. In response to this limitation, the SRGM approach is adapted to center around testing practices rather than relying exclusively on the progression of time. Earlier methods proposed restricted testing capabilities, but the notion of unlimited test time introduces an infinite testing scenario, rendering it impractical. To overcome this, the method introduces Neural Heterogeneous Poisson Process (NHPP) models, which operate without being bound by time limitations. Furthermore, the approach incorporates Artificial Neural Networks (ANNs) to model program failures based on available information. The application of an AI model selection method ensures precise representation of both historical and future load fitting location information Artificial Neural Networks (ANNs) showcase an adept capacity for effective generalization from the input provided to a comparable model, proficiently capturing patterns from prior instances of failure data. Through the integration of common-sense information into programming failure indicators, the suggested Testing Effort Function (TEF) and Software Reliability Growth Model (SRGM) proficiently depict extensive failure data. The incorporation of Machine Learning (ML) and Artificial Neural Networks (ANN) notably enhances the precision of traditional boundary estimation when compared to release time, thereby ensuring robust software reliability. © 2024 IEEE.

关键词： ANN Machine Learning NHPP SRGM Testing Effort Function (TEF)

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Revolutionizing Medical Diagnostics: Exploring Creativity in AI for Biomedical Image Analysis

Revolutionizing Medical Diagnostics: Exploring Creativity in...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Krishnamoorthy, P. Swetha, Devulapally Geetha, Pamidimukkala Sai Karunambiga, K. Ayyasamy, Ramesh Kumar Kiran, Ajmeera Thiruthangal Nadar College PG Department of Computer Science Tamil Nadu Chennai India Vaagdevi Engineering College Department of Computer Science and Engineering Telangana Bolli Kunta India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Karpagam Institute of Technology Department of CSE Tamil Nadu Coimbatore India Faculty of Information and Communication Technology Kampar Malaysia MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331540685

Biomedical image analysis plays a crucial role in the early and accurate diagnosis of diseases, significantly impacting patient outcomes. This study presents an innovative approach to biomedical image analysis by integrating machine learning techniques with a user-friendly web interface. The system takes input images uploaded through the web interface and processes them using advanced machine learning algorithms implemented in Python. The goal is to automate the detection of diseases from these images, enhancing the efficiency and precision of diagnosis. Convolutional Neural Networks (CNNs), a specialized class of deep learning models designed for image analysis, are employed to learn and characterize disease-specific patterns. The proposed approach is evaluated using diverse biomedical images encompassing various diseases and conditions. Extensive experiments demonstrate the system's effectiveness, achieving high accuracy, sensitivity, and specificity in disease detection. Integrating machine learning with a web interface simplifies the diagnostic process and enables remote consultations, making healthcare services more accessible and timelier. In conclusion, this study presents a robust framework for automated biomedical image analysis, demonstrating the potential of machine learning and web technologies in revolutionizing healthcare diagnostics. The system's accuracy and user-friendly interface make it a valuable tool for healthcare professionals, contributing to early disease detection and improved patient care. © 2024 IEEE.

关键词： Convolutional neural networks

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Neural network approaches for rumor stance detection: Simulating complex rumor propagation systems

Neural network approaches for rumor stance detection: Simula...

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作者： Li, Hao Yang, Wu Wang, Wei Wang, Huanran College of Computer Science and Technology Harbin Engineering University Harbin China

This research introduces a comprehensive suite of neural network models designed to tackle the challenging task of rumor stance detection within the framework of simulating complex rumor propagation systems. Our objective centers on accurately modeling the intricate structures of rumor dialogues and propagation patterns to identify user stances—whether they are in support, denial, questioning, or commenting on rumors. Unlike conventional methods that rely on simplistic keyword targeting and fail in the nuanced context of social networks, our models delve into the complexities of dialogue and propagation structures, offering a more precise and insightful analysis of rumor dynamics. In addressing the simulation and modeling of complex systems, our approach specifically focuses on the elaborate interaction networks that underpin rumor spread and reception. While our methodology does not directly engage with brain-like computing paradigms, it reflects a similar level of sophistication in handling layered and complex information flows, analogous to cognitive processes in understanding and interpreting human communications. Employing a hierarchical attention mechanism, our models adeptly parse through multitiered dialogue sequences, effectively distinguishing between various indicators of user stances. This allows for a nuanced and detailed representation of the rumor ecosystem, significantly enhancing the accuracy of stance detection. Through rigorous testing on diverse datasets, our approach has demonstrated superior performance over existing models, thereby establishing a new benchmark in the field. © 2024 John Wiley & Sons Ltd.

关键词： Neural network models

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Customized Movie Recommendation for Enhanced Entertainment

Customized Movie Recommendation for Enhanced Entertainment

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Minu, M.S. Talapalli, Srikar Chevuri, Saahithi Medhasri, T.S. SRM Institute of Science and Technology Department of Computer Science Engineering Ramapuram Chennai India SRM Institute of Science and Technology Department of Computer Science Engineering with Specialization in Big Data Analytics Ramapuram Chennai India

ISBN: (纸本)9798331542344

The surging popularity of online movie databases has created a challenge for viewers: choosing a film from a massive library can be overwhelming. In this paper, it proposes to design a new hybrid movie recommendation powered by machine learning techniques as a solution to improve user experience. Among those techniques, two are combined into this paper: content-based filtering, using the real features of a movie such as genre and a director, and collaborative filtering, which uses the rating provided by users. This hybrid approach thus improves the intrinsic weaknesses that reside in both these methods individually. On the other hand, content-based filtering tends to reduce exploration to movies of the exact type one is doing well at, whereas, inversely, collaborative filtering settings often mean that a user will be exposed only to films within his or her already-recognized taste profile. It is expected that the proposed hybrid system will solve both problems by balancing this line between exploring familiar cinematic territory and making new recommendations based on both user preferences and the entire collective intelligence of the user base. The approach would aim to enrich user satisfaction by facilitating the selection of movies and providing a richer and more diversified movie-viewing experience. © 2024 IEEE.

关键词： Collaborative filtering

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Automail AI: Intelligent Voice-Controlled Email Generator 3

Automail AI: Intelligent Voice-Controlled Email Generator

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Jayaram, Kavitha Mandagere, Samarth Gopisetti, Sampriti Sahit, S. Bnm Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331515911

The complexity of contemporary communication further emphasizes the need to automate monotonous work to increase efficiency and effectiveness. This paper introduces a new advance, voice-controlled Automail AI, in the form of an email generation tool that automatically sends an email according to end-user-specified keywords. Automail AI utilizes NLP, ML, and Speech Recognition to differentiate formal emails from informal ones and draft appropriate responses as the situation demands. It has been tuned with Naive Bayes Classifier that yields a better level of accuracy as compared to models such as Decision Tree, SVM, and Logistic Regression. Setting the benchmark for Automated Communication tools, Automail AI combines diverse technologies like SMTP to guarantee secure Email delivery, Google Text-to-Speech or GTTS for auditory feedback, and a module providing an interface for speech recognition that offers the possibility to use the tool with no hands. © 2025 IEEE.

关键词： Automail AI email generation GTTS machine learning Naive Bayes natural language processing SMTP speech recognition

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