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2nd IEEE International Conference on Device intelligence, Computing and Communication Technologies, DICCT 2024

作者： Padmavathy, C. Mahalakshmi, A. Vadivel, R. Kumawat, Rajat Dutta, Ritam Roy, Bhaskar Dutta, Nilanjan Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Computer Science and Engineering B.N.M Institute of Technology Bangalore India Computer Science and Engineering Poornima University Jaipur India Artificial Intelligence & Machine Learning Asansol Engineering College Asansol India Electronics & Comm. Engineering Asansol Engineering College Asansol India

ISBN: (纸本)9798350372847

As the technology behind informatics systems has improved, the Internet of Things (IoT) has grown in significance and application. IoT applications are becoming more popular as a result of the expansion of IoT enablers including smart watches, smart phones, security cameras, and smart sensors. Unfortunately, other issues have raised due to the unsecured nature of IoT devices. Distributed Denial of Service (DDoS) assaults are one of the deadliest cyber-attacks as an example. The untrustworthiness of IoT systems causes a number of security issues. Protecting IoT networks from dispersed denial of service attacks has been the focus of several recent research and development efforts. The primary challenge in this area of study is creating a model that is responsive to different types of DDoS assaults while also being able to distinguish between malicious and benign traffic to prevent false positives. In this study, a Convolutional Neural Network with only one dimension is used. The suggested system is employed in both statistics and cognitive science, and it is based on the central nervous system in particular. They are modelled as mathematical functions that are structured to depict complicated interactions between inputs and outputs (dependent variables), and they are exemplified by the connectivity of brain systems from a variety of input variables to the output (dependent variables) We foresee three types of assaults in our article. All models are tested with precision, recall and F-score analysis. SVM achieved nearly 86% of precision, 95.15% of recall and 90.43% of F-score, where the projected model achieved 92.53% of precision, 97.17% of recall and 91.59% of F-score. © 2024 IEEE.

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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Deep learning based RAGAE-SVM for Chronic kidney disease diagnosis on internet of health things platform

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Multimedia Tools and Applications 2024年 1-39页

作者： Kandukuri, Prabhakar Abdul, Ashu Kumar, Kuchipudi Prasanth Sreenivas, Velagapudi Ramesh, G. Gundu, Venkateswarlu Department of Artificial Intelligence and Machine Learning Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India Department of Computer Science and Engineering SRM University Andhra PradeshNeerukonda Mangalagiri 522503 India Department of Computer Science & amp Engineering Koneru Lakshmaiah Education Foundation Andhra PradeshVaddeswaram 522302 India Department of Computer Science and Engineering SRK Institute of Technology Andhra Pradesh Vijayawada521108 India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology TelanganaBachupally Hyderabad500090 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra PradeshVaddeswaram 522302 India

Chronic kidney disease (CKD) is a prominent disease that causes loss of functionality in the kidney. Doctors can now more easily gather patient health status data due to the growth of the Internet of Health Things (IoHT). The IoHT data contains a huge number of redundant data, making it challenging to predict CKD disease quickly and accurately. In healthcare applications like feature-based classification, a variety of disease diagnosis systems were used to address this problem. Current disease detection algorithms suffer from imbalanced dataset processing, low-accuracy feature learning, and high computational power requirements. Thus, deep learning-based clinical decision support systems have been developed to solve these complexities. To remove outliers from medical data, data collected with IoHT devices is first pre-processed using an enhanced K-means clustering technique. The Synthetic Minority over Sampling Technique is used to balance data because the IoHT dataset is highly imbalanced. The classifier detects anomalies in less time because of the implemented processing step. The balanced CKD dataset is then presented for use with a novel classifier called Residual Attention-Gated Autoencoder with Support Vector machine. In order to improve accurate detection, the adopted classifier can learn and extract features. The proposed method results in an increased accuracy of 99.43% within 70.1 s of computation time. The mean intersection of the union and kappa coefficient of the proposed method is 98.15% and 98.1%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Classification (of information)

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Self Defence and Safety Monitoring System 4

Self Defence and Safety Monitoring System

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4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Suhas, M.L. Kashyap, Akshat Devadiga, Dhanush Ghoshal, Manish Roopashree, S. Deemed-to-be University Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Jain Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665474368

In the modern world, the safety of women has been compromised. Domestic violence, sexual assault, and murder against women are more common than they are supposed to be. Women's safety is a major concern in India and there are many laws and measures in place to stop them, but the number of such incidents is only increasing by the day. This is not just limited to women as men face a lot of harassment too. In response to these problems, we aim to create a smart safety device that provides some security to the user. Many such smart devices are already available in the market but are not proven to be effective and often provide serious damage to the attacker. We aim to create a safety device that is capable of alerting the user or taking actions automatically without or with very little involvement from the user to activate it. This helps when there is a situation where the user doesn't get enough time to react. Different models available are analysed and the proposed solution is explained in detail. The drawbacks and future works are also included in the article. © 2022 IEEE.

关键词： Emotion Recognition

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Virtual Drive: Immersive VR-Controlled Car with Real-Time Interaction and Live Video Feed

Virtual Drive: Immersive VR-Controlled Car with Real-Time In...

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2024 International Conference on Computational intelligence and Computing Applications, ICCICA 2024

作者： Jain, Shweta Gandhi, Anju Bhandari Mehla, Stuti Aggarwal, Rishit Kwatra, Tanu Panipat Institute Of Engineering And Technology Department Of Artificial Intelligence And Machine Learning Panipat India Panipat Institute Of Engineering And Technology Department Of Computer Science And Engineering Panipat India Infosys Limited Pune India D.A.V Centenary College Department Of Computer Science And Applications Faridabad India

ISBN: (纸本)9798350306446

This project investigates the creation and application of a revolutionary virtual drive experience that blends immersive virtual reality (VR) technology with a real-world automotive simulator. The technology promises to deliver an unrivaled driving experience by allowing users to manage a virtual car in real time and view live video feeds from cameras mounted on the real car. Motion controllers, an excellent VR headset with motion tracking, and a customizable driving simulator with haptic feedback are examples of hardware components. The virtual experience is improved by the cameras and sensors in the actual automobile, which capture ambient data and real-time video feeds. Software development includes both realistic virtual reality surroundings and a sophisticated control mechanism that translates human motions into virtual activity. Real-time interaction features and the incorporation of live video streams boost user engagement. Network connectivity ensures smooth interaction and video streaming. Easy-to-use interfaces facilitate information access and control. Several tests and optimizations ensure performance and safety. The deployment process includes continuing adjustments based on user feedback and integration with VR platforms. The process of creating a unique virtual drive experience is described in this paper, along with examples of hardware-software integration that open up new possibilities for VR-controlled applications in the future. © 2024 IEEE.

关键词： Virtual reality

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Detection of Mango Leaves Disease using CSUBW based Residual Attention with EfficientNet 2

Detection of Mango Leaves Disease using CSUBW based Residual...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Kumar V, Sunil Jyothi, A.P. Nagamani, H.S. Sumanth, S. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Ramaiah University of Applied Sciences Faculty of Engineering and Technology Department of Cse Bengaluru India Department of Computer Science Bengaluru India Department of Computer Science and Computer Applications Kolar India

ISBN: (纸本)9798350382952

Mangoes are one of the most popular fruits worldwide due to their taste and nutrition. Plant diseases may reduce plant quantity and quality. Disease management to improve crop quality and productivity requires early plant illness detection. Automation of plant disease detection reduces large farm surveillance. Since plants feed us, leaf problems must be diagnosed quickly and accurately. Our advanced learning method automatically detects leaf disease in several mango plant varieties. This study creates the RAM-EfficientNet, a novel illness classification network. The RAM block in EfficientNet extracts additional features and handles slight leaf lesion changes. Transfer learning was used to train EfficientNet on ImageNet to overcome the mango leaf disease sample size issue. The Cat Swarm Updated Black Widow Model (CSUBW), a hybrid optimization model, changes RAM-EfficientNet's weights to improve classification accuracy. Black Widow Optimization Algorithm (BWO) and Cat Swarm Optimization Algorithm (CSO) are combined to create the hybrid optimization model. The model's performance is then assessed to confirm its efficacy. © 2024 IEEE.

关键词： Fruits

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Harris Hawks Optimization Based Feature Selection on High Dimensional data 2

Harris Hawks Optimization Based Feature Selection on High Di...

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

作者： Jena, Suvendu Kumar Kumar, Gotte Ranjith Al-Fatlawy, Ramy Riad Ali, S.Rasheed Mansoor Aruna, T.M. Indira Gandhi Institute of Technology Department of Computer Science Engineering and Applications Odisha Sarang India School of Computer Science & Artificial Intelligence Sr University Warangal India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Jamal Mohamed College Affiliated to Bharathidasan University Department of Computer Science Tiruchirappalli India Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9798350373110

In recent times, evolutionary algorithms have been demonstrating significant benefits in feature selection due to their simplicity and global search capability. However, most of the existing evolutionary algorithms are computationally intensive for high-dimensional data. In order to decrease computational costs, effective evaluation methods are crucial. In this paper, Harris Hawks Optimization (HHO) technique is proposed for effective feature selection on high dimensional data. Firstly, a Multi-Layer Perceptron (MLP) is used to form an integer optimization problem for efficient feature selection process. Then the proposed HHO method is applied for the process of feature selection. This method has three steps such as Initialization, solutions update and classification. The performance of proposed HHO algorithm is compared with the existing classifiers such as Binary Waterwheel Plant Algorithm (BWWP A) and Community detection based Genetic Algorithm (CDGAFS) and achieved 98.2% of accuracy. © 2024 IEEE.

关键词： Genetic algorithms

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Question Answering FAQ Bot Using Natural Language Processing 11th

Question Answering FAQ Bot Using Natural Language Processing

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Vijay, K. Sowmia, K.R. Ravikumar, S. Arockia Raj, Y. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9789819717231

The BOT retorts with the precise results that help the user for the query and also requests them to rate the response to provide better results. The [frequently answered questions] BOT is a program developed through machine learning and natural language processing. It classifies the queries and results as key-value pairs and maps the response to the user query. It extricates itself by providing additional capabilities such as NLU processes and reinforcement learning. NLU helps the bot to make it convenient for an array of languages to meet the diverse expectations. Reinforcement learning retrieves user rating to provide better performance and search results. The project was developed due to a concern about fetching meticulous responses for specific service-related queries. The overall satisfaction rate of users in getting responses to the questions they post is devastating. The root cause for the proposed project has aimed at providing an environment for a people to find their answers at better accuracy and also respond to their satisfaction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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Mitigating Light Detection and Ranging Spoofing Attacks in Connected Vehicle Systems Using machine learning-Based Anomaly Detection 2

Mitigating Light Detection and Ranging Spoofing Attacks in C...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Karthik, R. Marshal, S. Asmitha Shree, R. Vidhya, R. Saranya, N. Gomathy, A. Karunya Institute of Technology and Sciences Deemed to be University Division of Computer Science and Engineering Coimbatore India Kumaraguru College of Technology Department of Computer Science and Engineering Coimbatore India Hindusthan College of Engineering and Technology Department of Artificial Intelligence and Machine Learning Coimbatore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

Light Detection and Ranging (LiDAR) technology is one of the integral parts of systems involving connected vehicles. It provides the necessary two-dimensional accuracy to enable proper navigation, object identification, and collision avoidance through LIDAR technology. However, it is highly used and highly susceptibility to spoofing attacks: interference on signals radiated by LIDAR technology to confuse the Vehicle Perception System. This paper proposes a machine learning-based anomaly detection framework which is capable of discernment and countering of spoofing attacks. The proposed framework collects data from a set of sensors, including cameras, radar, and LiDAR, and is used to formulate a holistic, multidimensional perception model. In that sense, the system utilizes advance machine learning methods, like ensemble classifiers, neural networks, and, more importantly, autoencoders, to find anomalies in real-time that might point to spoofing activity. Simulations and empirical experiments, both benign and adversarial, evaluate the systematic approach to spoofing sophistication levels where LiDAR systems indicate noteworthy improvements in detection accuracy and robustness compared with the traditional techniques, thereby reducing false positives and negatives. In addition, through sensor fusion, the system demonstrates resilience by sustaining consistent performance even through the most complex and dynamic environments. This type of study really highlights that how the power of machine learning would be important for improvement in the cybersecurity framework of connected vehicles scalable and adaptable toward the protection of LiDAR-based systems from growing threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhanced Vision Transformer with Lion Optimizer for Accurate Classification of Monkeypox Skin Images 4

Enhanced Vision Transformer with Lion Optimizer for Accurate...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Bhatnagar, Roheet Hassan, Esraa Saber, Abeer Shams, Mahmoud Y. Elbedwehy, Samar Manipal University Jaipur Department of Computer Science and Engineering Rajasthan India Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Machine Learning Kafrelsheikh33516 Egypt Damietta University Faculty of Computers and Artificial Intelligence Information Technology Dept. Damietta34517 Egypt Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Data Science Kafrelsheikh33511 Egypt

ISBN: (纸本)9798350387490

Monkeypox is a contagious viral infection caused by the monkeypox virus (Mpoxvirus). Characterized by skin lesions, fever, respiratory difficulties, swollen lymph nodes, and neurological complications, MPX can be fatal. Recent outbreaks have spread the disease across Europe, Australia, the United States, and Africa. Traditionally diagnosed through PCR testing of skin lesions, this process poses significant risks to medical personnel due to potential exposure to the highly contagious virus. However, the usage artificial intelligence (AI) is revolutionizing diagnostics, providing a safer and more efficient approach to MPX detection. In this paper, a new AI technique based on vision transformer and lion optimizer for classifying Monkeypox Skin Images. The presented model used for classifying the dataset and results reached accuracy of 99.6%. © 2024 IEEE.

关键词： Classification Deep learning Diagnosis Monkeypox

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