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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Aruna Gayatri Kodukulla M. Amanullah S A Kalaiselvan Department of Computer Science & Engineering Saveetha School of Engineering SIMATS Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

In recent years, UWSNs have emerged as a game-changer in many aquatic applications, including exploration, environmental monitoring, target tracking for underwater navigation, terrain-assisted navigation, and many more. The intricacy of the underwater environment presents several obstacles for the deployment of sensor nodes, and UWSNs are dynamic networks, unlike terrestrial wireless sensor networks. Instead of transferring data from every node to a destination node, autonomous underwater vehicles (AUVs) provide a more reliable option for data collecting in UWSNs. Nevertheless, a major challenge in data collection for UWSNs is the development of AUV positioning algorithms that are both delay-aware and energy-efficient. A trustworthy AUV-based data-collection routing strategy employed for UWSNs with the goal of decreasing network latency and increasing network lifespan. A positioning mechanism is used by the proposed protocol to gather data utilizing AUVs. With the goal of lowering power usage, the sink node coordinates AUV data collecting from the sensor nodes. To improve scalability, sensor nodes are first grouped into clusters, and then an AUV is assigned to each cluster. Second, in order to ensure the trustworthy gathering of data, the Actor 3 Critic (A3C) for every AUV is designed using the Markov decision process (MDP). In regards to energy consumption, delay, coverage percentage, and dependability, the simulation results validate the efficacy and efficiency of the proposed method.

关键词： Wireless communication Wireless sensor networks Energy consumption Simulation Ubiquitous computing Throughput Energy efficiency Underwater navigation Reliability Vehicle dynamics

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Bottom-Up Cracks Detection in Road Pavement in artificial Neural Network 1

Bottom-Up Cracks Detection in Road Pavement in Artificial Ne...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Sunil Kumar, V. Alzubaidi, Laith H. Ramesh, G.P. Sreenath, M.V. Gayathri, T. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India The Islamic University of Babylon Department of Computers Techniques Babylon Iraq Department of Electronics and Communication Engineering Chennai India Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Bangalore India Dayananda Sagar University School of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350352931

In recent years, road crack detection is a significant task for detecting surface defects, road safety and road maintenance. But there is lack of approach that able to detect the concealed cracks especially bottom-up cracks, and this is challenging due to poor continuity of cracks, various crack's widths, and low contrast among cracks and the surrounding pavement. The artificial Neural Network (ANN) algorithm is proposed for detecting and classifying the structural health status of bottom-up crack in road pavements, which allows detecting surface defects and failures. The approach aims at collection of these signatures by utilizing acoustic-sensors, located at roadside, and also classification of SHS in pavements over the ANN approaches. The various machine learning (ML) classifiers such as Multilayer Perceptron (MLP), 2-Dimensional Convolutional Neural Network (2D-CNN), Support Vector Classifier (SVC) and Random Forest Classifier (RFC) are utilized as well as compared for the analysis of crack in road pavement. The experimental results illustrate the probability of associating with great accuracy 92.7 %of MLP, 96.7 %of 2D-CNN, 92.5 %of RFC and 9 9. 3 %of SVC respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Modified EfficientNetB3 Deep learning Model to Classify Colour Fundus Images of Eye Diseases 5

Modified EfficientNetB3 Deep Learning Model to Classify Colo...

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5th IEEE International Conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2023

作者： Sharma, Riya Gangrade, Jayesh Gangrade, Shweta Mishra, Ashish Kumar, Gautam Kumar Gunjan, Vinit Manipal University Jaipur Dept of Computer Science & Engineering Jaipur India Learning Manipal University Jaipur Dept. of Artificial Intelligence & Machine Jaipur India Manipal University Jaipur Dept of Information Technology Jaipur India Jaypee Institute of Information Technology Dept of Computer Science & Engineering Noida India CMR Institute of Technology Department of Computer Science & Engineering Hyderabad India

ISBN: (纸本)9798350338287

The only way to prevent blindness from eye problems is by early detection and prompt treatment. Although colour fundus photography (CFP) is useful for fundus inspection, there is a need for computer-assisted automated diagnosis tools due to the similarities between the early symptoms of many eye disorders. The suggested approach uses cutting-edge deep learning model to categorize images into several disease categories by learning distinguishing features from the input images. The high-resolution fundus photos from individuals with diabetic retinopathy (DR), glaucoma, cataract, and healthy eyes make up most of the dataset used for this research. The experimental findings show that the suggested system achieve 97% accuracy with modified efficientNetB3 model and surpasses current approaches for categorizing eye diseases. This approach may help doctors diagnose and treat eye conditions earlier, leading to better patient outcomes. © 2023 IEEE.

关键词： Color

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A knowledge-driven framework for synthesizing designs from modular components 34

A knowledge-driven framework for synthesizing designs from m...

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34th CIRP Design Conference, CIRP 2024

作者： Chaumet, Constantin Rehof, Jakob Schuster, Thomas Software Engineering by Algorithms and Logic Department of Computer Science TU Dortmund University Otto-Hahn-Str. 12 Dortmund44227 Germany Lamarr Institute for Machine Learning and Artificial Intelligence TU Dortmund University Joseph-von-Fraunhofer-Straße 25 Dortmund44227 Germany

Creating a design from modular components necessitates three steps: Acquiring knowledge about available components, conceiving an abstract design concept, and implementing that concept in a concrete design. The third step entails many repetitive and menial tasks, such as inserting parts and creating joints between them. Especially when comparing and implementing design alternatives, this issue is compounded. We propose a use-case agnostic knowledge-driven framework to automate the implementation step. In particular, the framework catalogues the acquired knowledge and the design concept, as well as utilizes Combinatory Logic Synthesis to synthesize concrete design alternatives. This minimizes the effort required to create designs, allowing the design space to be thoroughly explored. We implemented the framework as a plugin for the CAD software Autodesk Fusion 360. We conducted a case study in which robotic arms were synthesized from a set of 28 modular components. Based on the case study, the applicability of the framework is analyzed and discussed. © 2024 Elsevier B.V.. All rights reserved.

关键词： computer circuits

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An Involuntary Liver Cancer Recognition in Abdominal Liver Imageries with Soft Optimization Methods in Biomedical Application

An Involuntary Liver Cancer Recognition in Abdominal Liver I...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Kumar, M. Vinodh Kumar Patra, Raj Veena, R. Siva Kumar Reddy, A. Sunil Kumar, V. Inventuriz Labs Pvt Ltd Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350398465

A medical image recovery system can be used for a variety of useful purposes, one of which is the classification of medical images. When numerous different types of medical imaging evidence are accessible, it is essential to have accurate categorization models. Because of the CT paradigm in clinical diagnosis, radiologists are able to properly detect and monitor changes in the body's physiological state. The ability to differentiate between different types of tissues in a CT image based on the various grey rates along each line contributes significantly to the accuracy of a medical diagnosis. The standard practice for diagnosing cancer relies on scientific and histological data, both of which have the potential to lead to incorrect or erroneous findings. The liver, which may be found in the upper abdominal region of the external human form, is responsible for filtering the blood and getting rid of any toxins it discovers in the process. The distinction between the two types of cancer is taught through an artificial neural network in this method. A number of different parameters, including accuracy, area, correlation, entropy, homogeneity, contrast, and the similarity index, are employed to conduct an analysis of the impact. After that, researchers tried to enhance the early identification and treatment of liver cancer by segmenting and classifying liver tumors from CT images. This was done in order to gain a better understanding of the disease. The accuracy of the identification was raised to 99.63 percent thanks to the classifier and optimizer that was proposed. © 2023 IEEE.

关键词： Medical imaging

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Designing Autotuned Student Models by Knowledge Distillation from Self-Supervised Teacher Models

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SN computer science 2025年第5期6卷 1-16页

作者： Kishore, Jaydeep Mukherjee, Snehasis Department of Computer Science and Engineering Shiv Nadar Institution of Eminence Delhi NCR Greater Noida India Department of Artificial Intelligence and Machine Learning Manipal University Jaipur Rajasthan Jaipur 303007 India

As deep learning-based models continue to be developed, optimizing model accuracy and efficiency remains a challenging task. This paper applies a knowledge distillation-based approach to train a student model from a pretrained self-supervised teacher model for application to image classification. The study further describes how hyperparameters such as the number of layers, number of units, dropout, and learning rate for the multi layered perceptron (MLP) classifier following the student model are automatically tuned (autotuned), along with the hyperparameters used in knowledge distillation (balancing factor alpha and beta, and the temperature) using an efficient combination of Gaussian process and tree-structured Parzen estimator (TPE) algorithms. Additionally, the paper uses a linear combination of hard target, soft target, Barlow-Twins loss and cosine similarity loss functions. The experiments were conducted on three benchmark datasets: CIFAR-10, CIFAR-100, and Caltech-101, and the results are promising compared to the state-of-the-art self-supervised models. The source code for this study is publicly available at github. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bayesian optimization BO-GP BO-TPE Ensemble learning Hyperparameter optimization Knowledge distillation Self-supervised

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machine learning-Enhanced Self-Management for Energy-Effective and Secure Statistics Assortment in Unattended WSNs

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SN computer science 2025年第2期6卷 1-10页

作者： Biswas, Preesat Mishra, Aishwarya Dixit, Rinku Sharma Dwivedi, Abhishek Choudhary, Shailee Lohmor Tiwari, Shweta Chakravarthi, M. Ayyappa Electronics & Telecommunications Government Engineering College C. G Jagdalpur India Department of Computer Science & Engineering IES College of Technology M.P Bhopal India Department of Artificial Intelligence and Machine Learning New Delhi Institute of Management Delhi India Department of Data Science and Engineering School of AIML IoT & IS CCE DS and Computer Applications Manipal University Jaipur Jaipur Rajasthan India Department of Computer Science and Engineering Gyan Ganga Institute of Technology and Sciences M.P Jabalpur India Department of CSE-Data Science KKR and KSR Institute of Technology and Sciences Guntur India

Unattended Wireless Sensor Networks (UWSNs) operate without human supervision and have limited resources. In a conventional WSN with a fixed sink for data collection, nodes one hop away from the sink consume more energy, causing a hotspot issue. This leads to network fragmentation and reduced sensing efficiency. To address this, a mobile sink is proposed. This study details the development and implementation of Secure MRL, focusing on legitimate message communication to the mobile sink via convex nodes. By using these convex nodes as data collection points, the mobile sink avoids traversing the entire network. We employ an enhanced system to generate keys and authenticate messages, assessing its resilience against various attacks, including sleep deprivation, snooze, network substitution, and modification. Findings indicate that Secure MRL maintains consistent performance across different node counts and significantly reduces energy consumption by 84%, improving the speed of attack detection. This improvement is achieved by monitoring the digital signature and residual energy of nodes. The proposed SVM-based network training establishes a residual energy cut-off, while convex nodes mitigate synchronization issues among other sensor nodes in Secure MRL. Additionally, convex nodes manage the availability checks for the mobile sink. The proposed approach demonstrates a 92% improvement in packet delivery ratio (PDR) and an 89% reduction in end-to-end delay, highlighting its efficacy in enhancing network performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Duty-cycled UWSN MRL Sec-MRL SEC-TMP SVM UWSN

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E-Commerce Product Review Classification based on Supervised machine learning Techniques 9

E-Commerce Product Review Classification based on Supervised...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Dharrao, Deepak Deokate, Sarika Bongale, Anupkumar M Urolagin, Siddhaling Symbiosis Institute of Technology Department of Computer Science and Engineering Pune India Pimpari Chinchwad College of Engineering Department of Computer Science and Engineering Pune India Symbiosis Institute of Technology Department of Artificial Intelligence and Machine Learning Pune India Department of Computer Science and Engineering Birla Institute of Technology & Science Dubai International Academic City P. O. Box No 345055 Dubai United Arab Emirates

ISBN: (纸本)9798350397376

To become a successful organization it is the most important aspect to consider is the customer satisfaction. A business can grow only when the customers enjoy their product/service and how much they recommend it. For the same, companies collect reviews and ratings from its customers to understand their perspective better. Amazon is also such a giant, which values its customers' opinions. It is very difficult or nearly impossible to have a detailed manual scan of each and every review for each and every product available on the website. This is where NLP (Natural language processing) comes into picture. It processes human language from text to arrays of numbers in order to find sense in the text so that the machine learning algorithms can process them further and classify the texts. This project focuses on processing the amazon reviews of various categories and products and then classify them into 5 ratings, from 1 to 5, worst to best respectively. The supervised machine learning algorithms used to process these reviews are Support Vector machines (SVM), Naïve Bayes classifier and Decision Tree classifier. The analysis of comparison of these algorithms has also been discussed using accuracy score bar charts. © 2023 IEEE.

关键词： Sentiment analysis

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An Ensemble Approach of CNN and GRU Models for Network and Server Intrusion

An Ensemble Approach of CNN and GRU Models for Network and S...

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Emerging Technology (INCET), International Conference for

作者： Aniketh Reddy Adireddy Vishnu Kurnala Sindu Patlolla Gagandeep Arora Artificial Intelligence & Machine Learning Vardhaman College of Engineering Hyderabad India Computer Science & Engineering (AI&ML) Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

This research paper delves into the realm of cybersecurity by proposing an innovative ensemble model that blends Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN) for network and server intrusion detection. CNNs excel in spatial feature extraction, particularly suited for analyzing network traffic patterns, while GRUs are adept at capturing temporal dependencies within sequential data. The integration of these models aims to leverage their complementary strengths, enhancing the overall robustness of intrusion detection systems. Through comprehensive training and evaluation on diverse datasets, the ensemble model showcases promising results, demonstrating improved accuracy and resilience against adversarial attacks compared to standalone CNN and GRU models. In addition to making a significant contribution to the rapidly developing field of deep learning in cybersecurity, this research offers practical implications for the development of more secure network and server environments in the face of dynamic cyber threats.

关键词： Training Intrusion detection Telecommunication traffic Logic gates Feature extraction Robustness Convolutional neural networks

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A Secure and Efficient Framework for Storing Medical Data using Blockchain and Cloud Servers

A Secure and Efficient Framework for Storing Medical Data us...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： M Lakshmanan G.S. Anandha Mala G. Sivakumar V S Divya Sundar Rupa Kesavan A Ajoe Sweetlin Jeena Department of Artificial Intelligence and Machine Learning Dayananda Sagar University Bangalore Department of Computer Science and Engineering Easwari Engineering College Anna Univeristy Chennai Department of Artificial Intelligence and Machine Learning Gnanamani College of Technology Anna University Saveetha Engineering College Anna Univeristy Chennai Computer Science and Engineering Sri Venkateswara College of Engineering Sriperumbudur India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

The digitization of Electronic Health Records (EHRs) has brought a revolution in health care delivery and even practices in reporting key organizational HR processes. However, these advancements have their merits and demerits particularly in regard to data security, the speed of data retrieval, and how data transactions are performed. This paper presents a solution to this problem in the medical world. It presents a unique architecture that connects cloud servers with Blockchain technology for better and secure medical databases. Using Merkle trees to protect health information and advanced heuristic algorithms to optimize operations, the system deals with main issues of transaction time, retrieval rate, and data privacy. The method is tested on a realized medical database and shows that the performance indicators increased substantially. It would seem that Blockchain assures that data cannot be altered once uploaded, while cloud servers provide flexibility to users and allow faster data retrieval. Proposed system improves the transaction time by 30 percent and the retrieval rate by 40 percent compared with traditional methods and supports strategic management of EHR systems.

关键词： Data privacy Databases Scalability Heuristic algorithms Medical services Data retrieval Threat assessment Blockchains Servers Electronic medical records

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