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3rd International Conference on artificial intelligence For Internet of Things, AIIoT 2024

作者： Manaswitha, K. Issac, Titus Silas, Salaja Terance, J Sebastian Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350372120

In recent years, there have been significant advancements in Unmanned Aerial Vehicles (UAVs), leading to their integration into everyday life. UAVs are constrained by limited energy, communication, and localization. The various challenges in UAV research include localization, routing, navigation, and task assignment. Recently, UAV swarms have been employed across a wide range of applications. This study focuses on solving task assignment problems within UAV swarms, considering the unique multi-objective resource constraints of UAVs. The work explores the feasibility of adopting the Red Fox Algorithm to address the task assignment problem in UAV swarms. The investigation examines two primary input parameters: Task Energy Demand and distance. The evaluation includes energy utilization and shortest distance as performance metrics. Notably, the proposed approach outperforms the Particle Swarm Optimization Algorithm (PSO), resulting in a 17.47% lower energy utilization while maintaining a 17.83% reduction in the shortest distance travelled. By leveraging the Red Fox Algorithm, the work aims to enhance the efficiency and effectiveness of task allocation in UAV swarms, contributing to their broader adoption across various applications. © 2024 IEEE.

关键词： Energy utilization

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Feature Extraction and Similarity-Based Movie Recommendation Using Bag-of-Words Model

Feature Extraction and Similarity-Based Movie Recommendation...

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Raj, Swati Dutta, Jhalak Bandopadhyay, Saikat Prasad, Rajesh Department of Computer Science and Engineering Christ University Bengaluru India Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Artificial Intelligence and Machine Learning Netaji Subhash Engineering College Kolkata India Department of Computer Science and Engineering MIT Art Design and Technology University Pune India

ISBN: (纸本)9789819613472

Recommender systems have become pervasive in guiding users through a lot of choices available in today’s digital landscape. This paper presents a content-based recommender system focused specifically for movie recommendations, offering a viable alternative to collaborative filtering methods. Unlike collaborative filtering, which relies on user ratings and demographic data, the proposed system operates without such prerequisites, making it particularly advantageous in scenarios with limited user engagement. By leveraging content attributes such as title, genre, cast, and crew, the system identifies similarities between movies and recommends those akin to a user’s preferences. Through innovative preprocessing techniques and feature extraction methodologies, the system transforms textual descriptions into numerical vectors, enabling efficient recommendation generation. The proposed model contributes to addressing critical challenges inherent in collaborative filtering, including the cold start problem, data sparsity, and popularity bias. Furthermore, it underscores the broad applicability of recommender systems across various domains and emphasizes the continuous evolution of interface designs and algorithms within the research community. Overall, the proposed content-based recommender system offers a promising solution for delivering personalized movie recommendations while advancing the state of the art in recommendation technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bag-of-words Cosine similarity Filtering system Recommender system TF-IDF

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Improving Accuracy in Network Intrusion Detection via machine learning Algorithms: An In-Depth Examination

Improving Accuracy in Network Intrusion Detection via Machin...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Suriya Prakash, J. Trishlaa, S. Soniya, R. Lakshmanan, V. Kiran, S. Department of Cse Ramnagara India Rajarajeshwari College of Engineering Department of Artificial Intelligence and Machine Learning Bengaluru India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Nitte Meenakshi Institute of Technology Department of Mathematics Bengaluru India

ISBN: (纸本)9798350375442

Intrusion detection is essential for securing computer networks by monitoring and analyzing activity to detect and respond to unauthorized access and malicious behavior. By examining network traffic and system logs, intrusion detection systems (IDS) help protect data integrity and availability, enabling swift responses to potential security incidents. In an ever-evolving cyber threat landscape, IDS plays a critical role in maintaining network security. This research investigates the performance of various machine learning algorithms for classification tasks using a dataset from Kyoto collected on December 31st, 2015. The algorithms explored include Naive Bayes, K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), AdaBoost, and CatBoost. Training and testing the model with different data splits (80/20, 70/30, 60/40) revealed that AdaBoost, LGBM, XGBoost, Decision Tree, Gradient Boosting, and Random Forest consistently delivered the highest accuracy. The study concludes that selecting the appropriate algorithms and data splits is crucial for enhancing IDS effectiveness, providing valuable insights for future research. Explore the code behind this study at https://***/Trishlaa07/Kyoto_2015_Dec_day31 © 2024 IEEE.

关键词： Network intrusion

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Intrusion Detection in IoT Environment Using Stochastic Gradient Descent with Warm Restarts and Gated Recurrent Unit 2

Intrusion Detection in IoT Environment Using Stochastic Grad...

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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Krishnamurthy, Revatthy Alabdeli, Haider Rajani, N. Ramesh, Vankudoth Kumar, Bura Vijay Rajalakshmi Engineering College Department of Artificial Intelligence & Machine Learning Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Dept. of Ece Bengaluru India Cvr College of Engineering Department of CSE-DS Mangalpalle India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350373110

The rapid growth of Internet of Things (IoT) has attracted the cybercriminals which resulted in increase of cyber-attacks on IoT devices. If the cyber-attacks are undetected for a longer time, it causes serious service interruptions, identity protection threats. To handle these issues, this research proposes a novel intrusion detection system by employing Stochastic Gradient Descent with warm Restarts and Gated Recurrent Unit (SGDR-GRU) method. The data is normalized to obtain common scaled data by using z-score normalization technique and the feature selection is performed by Stochastic Gradient Descent with warm Restarts (SGDR). The SGDR is an optimization technique that escapes local minima, reduces overfitting and improves the performance by adjusting learning rate. Finally, classification of normal and anomaly data is carried out by Gated Recurrent Unit (GRU) that deals with gradient descent problem and captures long-range dependencies. The performance of the proposed model is evaluated on NSL-KDD dataset and achieved high accuracy 99.12%, precision 99.45%, recall 99.23% in identifying intrusion over IoT devices. The proposed method surpassed existing intrusion detection models such as stacked-deep polynomial network (SDPN), Hybrid Chicken Swarm Genetic Algorithm (HCSGA). © 2024 IEEE.

关键词： Genetic algorithms

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Forecast Model for Scheduling an HPC Application on CPU and GPU Architecture 3

Forecast Model for Scheduling an HPC Application on CPU and ...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Chandrashekhar, B.N. Mohan, M. Geetha, V. BMS Institute of Technology & Management Artificial Intelligence and Machine Learning Benguluru India Nitte Meenakshi Institute of Technology Information Science & Engineering Benguluru India Reva University School of Management Bengaluru India

ISBN: (纸本)9798350338607

Process scheduling is an essential part of multiprogramming operating systems. Scheduling is a process that allows one process to use the processing unit while the execution of another process is on hold (in a waiting state) due to the unavailability of any resource like I/O, thereby making full use of CPU or GPU. The major issue of scheduling is to make the system efficient, fast, and fair. This work focuses on developing a Forecast model and constructing scheduling strategies to schedule parallel applications on CPU and GPU. During the design of the Forecast model, we will consider the history of the actual execution time set of processes, then we compute the average time of individual sets of processes by considering parameters such as complete execution time, the sum of processes, and the number of threads assigned to individual processes. Then we will evaluate the Prediction time of CPU and GPU for individual sets of processes. By considering parameters such as the average time of the previous set of processes, the weight of processes, and the number of processes. Then based on the prediction time we will develop a scheduling strategy. As the minimum prediction time required set of process resources is assigned to the CPU and the GPU is assigned by the maximum predicted timed resource of the process. In this work we utilized the CPU and GPU resources effectively for stream benchmark application, our experiment shows that less than 20% average percentage prediction error in all cases. © 2023 IEEE.

关键词： Forecasting

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A Type-1 Fuzzy Logic Framework for Early Risk Assessment of Gestational Diabetes Mellitus Using Clinical Risk Factors 2

A Type-1 Fuzzy Logic Framework for Early Risk Assessment of ...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Suman, Saurabh Pacharaney, Utkarsha Gote, Pradnyawant M. Faculty of Engineering and - Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

Gestational Diabetes Mellitus (GDM) is a condition that can put at risk both mother and fetus but early and proper risk assessment need to be carried out for effective management. This paper discloses a Mamdani-kind Fuzzy Inference System to evaluate GDM risk dependent on clinical parameters like age, parity, glucose levels, body mass index (BMI), and insulin levels. The system can simulate all the numerous risk factors in a more gentle way by the utilization of the triangular membership function that converts accurate clinical data into a fuzzy format. A clear rule base, got from the knowledge maps in the clinical field, is put together from the input data to determining the GDM risk levels. This is neither then defuzzied via the centroid method to generate a determinable score. Results show that this system can be used along with the experts, thus yielding the same results as them, meanwhile, it is capable of providing an intelligible output. It lends further credence to the role of fuzzy logic in uncertainties in medical data and their potential by decision support systems in clinical context. While reasonably beneficial results from the system are reported, the static nature of the membership functions implies, for the future, adaptability in methodologies for relative accuracy. This study still emphasizes that fuzzy logic finds application in settings of clinical delineation, which are distinctly more complex for their intended usefulness for conditions such as GDM here. © 2025 IEEE.

关键词： Risk assessment

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Biomaterials and Their Applications

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Biomedical Materials and Devices 2025年 1-16页

作者： Menagadevi, M. Nirmala, M. Thiyagarajan, D. Somasundram, D. Department of Computer Science and Engineering School of Engineering Malla Reddy University Hyderabad India Department of Biomedical Engineering Dr. N G P Institute of Technology Coimbatore India Department of Artificial Intelligence and Machine Learning School of Engineering Malla Reddy University Hyderabad India Department of Micro & Nanoelectronics School of Electronics Engineering Vellore Institute of Technology Vellore India

A biomaterial is a biocompatible substance engineered to interact with biological systems for therapeutic and diagnostic, such as pacemakers, dental implants, vascular stents, artificial joints, drug delivery systems, tissue engineering, biosensors, image contrast agents in Computer Tomography, Magnetic Resonance Imaging, optical imaging, and ultrasound. Biomaterials are also used as assays in point-of-care testing devices. Based on the organs’ biological properties, the biomaterials are mainly classified into metals, polymers, ceramics, and composite biomaterials. The success rate of the implant depends on the physical, chemical, and mechanical properties of the selected material. Each biomaterials have unique properties, so it is used in both soft and hard tissue replacements. In this review, various properties and applications of biomaterials are discussed. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Applications Biocompatibility Biomaterials Properties Types

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Using Health Sensors, Internet of Things (IoT), and machine learning to Determine the Potential Dangers Older People Pose to Society 2

Using Health Sensors, Internet of Things (IoT), and Machine ...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Veena, K. Indu, B. Kesh, Snigdha Guha, Tapas Amc Engineering College Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Because it has the potential to minimise the issues that are today encountered by the healthcare system, the Internet of Things (IoT) has gained a lot of attention over the past few years as a result of the fact that it has received a lot of attention over the last few years. The ever-increasing prevalence of chronic diseases, on the one hand, and the continuous growth of the human population, on the other, have directly contributed to the significant amount of stress that has been placed on the health and fitness gadget. Despite the fact that there are a great number of issues in this field that need to be researched with a high level of seriousness, the medical care industry's inability to maintain standards has hampered all of this research. This is despite the fact that standards need to be maintained in the medical care business. The primary objective of this study is to determine the risk level of people of advanced age who are experiencing cardiac problems and who are staying at home when there is no one else around to take care of them. Specifically, this research will look at people who are staying at home when there is no one else around to look after them. This study will focus specifically on persons who are remaining at home when there is no one else there to care for them and will examine their circumstances. If anything were to happen during that period, nobody would be there to help him or her in any manner if it did take place. If it did take place, nobody would be there. As a consequence of this, our approach offers a contribution to the direct monitoring of the specifics of afflicted individuals' health at this precise moment. These specifics include the pulse rates of people, the temperature rates of humans, and a variety of other similar rates. If hospitals or administrators become aware of the situation and are able to grasp it, preventative measures may be taken in order to reduce the overall number of individuals who wind up passing away as a

关键词： Internet of things

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Impact of Ant Colony Optimization on Optimal Routing for Vehicular Ad Hoc Networks 5

Impact of Ant Colony Optimization on Optimal Routing for Veh...

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5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Ramamoorthy, Raghu Velu, Anitha Appala Naidu, R China Asha Kumari, A. Angel Donny, F. Harish Kumar, M. The Oxford College of Engineering Department of Computer Science and Engineering Bengaluru India Sri Sairam College of Engineering Department of Electronics and Communication Engineering Bengaluru India Ramaiah Institute of Technology Department of Computer Science and Engineering Bengaluru India Vemana Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India

ISBN: (纸本)9798350379884

Using biologically inspired approaches has bonus advantages in offering optimal message distribution for vehicular ad hoc networks (VANETs). Bio-motivated approaches are motivated by the natural behavior of biological entities such as bees and insects. Ant colony optimization (ACO) is the optimal bio-inspired style, suitable for offering optimal routing in VANETs. The communication pattern of ACO and VANETs is similar, such as ants of ACO apply indirect communication among ants for the exchange of information, and VANETs require cooperative routing techniques for message exchange. The goal of the Bio-motivated approach is to attain proficient and accessible communication in a VANET, where vehicles turn as nodes and animatedly form a system to interchange information. In this context, the proposed work discusses the effectiveness and benefits of using a bio-inspired approach such as ACO for VANETs. To demonstrate ACO, a healthcare scenario is considered. In the healthcare system, efficient communication is required between emergency vehicles and hospitals to offer optimal treatment for patients. This work applies ACO to find the optimal path for sending an alarm to the nearest hospital from the emergency vehicle to arrange necessary things to treat patients. Using ACO increases the efficiency of the routing mechanism in all aspects such as throughput, overhead, data delivery, low latency, route discovery time, route repair time, convergence speed, and stagnation issue. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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AI-Driven Transcription and Analysis for Medical Conversations 2

AI-Driven Transcription and Analysis for Medical Conversatio...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Pisal, Tejas Hajare, Rasika Verma, Prateek Gote, Pradnyawant M. Jiet, Mosses Chaudhri, Shiv Nath Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

This paper presents an advanced system that integrates Google's Speech Recognition API with the Gemini-l.5-flash model to improve audio processing and response generation, particularly in the context of healthcare communication. The system is designed to transcribe audio files of doctor-patient conversations accurately and generate contextually relevant responses using AI. A user-friendly interface, developed with Tkinter, allows seamless uploading and processing of these audio files. The integration of these technologies aims to enhance the efficiency and effectiveness of automated response systems, thereby improving patient-provider communication. The system's capabilities were evaluated using real-world healthcare datasets, demonstrating its potential to streamline the workflow from audio input to meaningful textual output. This work not only showcases the practical application of AI in healthcare but also lays the groundwork for future innovations in automated medical communication systems. © 2025 IEEE.

关键词： Natural language processing systems

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