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Recent Trends in computer science and Technology (ICRTCST), International Conference on

作者： A Chandrashekhar Amit Rawate A. Dhanamathi Rakeshnag Dasari Rajeshri Pravin Shinkar Pavithra G Department of Mechanical Engineering Faculty of Science & Technology Icfai Foundation for Higher Education Hyderabad Telangana Department of Electronics and Computer Engineering Maharashtra Institute of Technology Aurangabad Maharashtra Department Of Computer Science and Engineering Roever Engineering College Perambalur Tamilnadu Department of CSE Acharya Nagarjuna University Nagarjuna Nagar Guntur Department of Computer Science SIES College of Arts Science and Commerce Nerul Navi Mumbai Department of Electronics & Communication Engineering Dayananda Sagar College of Engineering Bangalore Karnataka

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

The application of Deep Reinforcement Learning (DRL) to enhance the autonomous navigation capabilities of drones in environments that are complex and congested. The complications involved with guiding drones across restricted places, overcoming obstructions, and navigating among clutter are addressed in this article. Through the utilization of DRL techniques, the framework that has been proposed gives drones the ability to learn and alter their navigation strategies on their own through the process of trial and error, hence optimization of real-time decision-making. The incorporation of deep neural networks for the processing of sensory data makes it possible for drones to comprehend their environment, which in turn makes it easier for them to make educated judgments with regard to safe navigation. In order to demonstrate the potential of DRL in boosting the autonomy and robustness of drone navigation systems in demanding environments, the effectiveness of the technique is evaluated using simulations and experiments conducted in the real world. The findings make a contribution to the development of autonomous drone technology, which has implications for a variety of applications including environmental monitoring, search and rescue operations, and surveillance.

关键词： Navigation Surveillance Decision making Deep reinforcement learning Market research Robustness Real-time systems

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Advanced Cyber-Attack Detection in IoT Networks Using Deep Belief Networks and Gradient Boosting Mechanisms

Advanced Cyber-Attack Detection in IoT Networks Using Deep B...

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Mobile Networks and Wireless communications (ICMNWC), IEEE International Conference on

作者： R. Mohandas M Elavarasi RVS Praveen Hemanand Chittapragada C. Nithiya S. Prabagar Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai India Department of BCA - IT Vels Institute of Science Technology & Advanced Studies Pallavaram India Digital Engineering and Assurance LTIMindtree Limited Hyderabad India Department of Computer Science and Engineering Madanapalle Institute of Technology & Science Madanapalle India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Puduvoyal India Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

The rapid growth of Internet of Things (IoT) devices has increased security weaknesses, rendering networks vulnerable to advanced cyber-attacks. Traditional detection systems frequently encounter difficulties in real-time anomaly detection and resource limitations, leaving IoT systems exposed. This study presents a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction with Gradient Boosting (GB) to improve classification accuracy. Driven by the increasing incidence and complexity of cyber threats, the study seeks to rectify significant deficiencies in IoT security by providing a scalable, efficient, and high-performance detection system. Experimental findings confirm the model's efficacy, attaining 98.5% accuracy, an F1-score of 0.97, and a recall of 0.97, markedly surpassing current methodologies. The study addresses the issues of protecting IoT networks, suggests a comprehensive detection methodology, and offers a comparative analysis of performance indicators, laying the groundwork for refined, versatile approaches to counter new threats in IoT ecosystems.

关键词： Wireless communication Accuracy Computational modeling Scalability Boosting Rendering (computer graphics) Complexity theory Internet of Things Reliability Cyberattack

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Primeval Identification and Diagnosis of Alzheimer's Disease Utilizing 3D CNN Models

Primeval Identification and Diagnosis of Alzheimer's Disease...

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International Conference on Advanced Computing and communication Systems (ICACCS)

作者： K. L. Sujitha Lynsha Helena Pratheeba HP Rajeshwari R Kottaimalai Ramaraj Pallikonda Rajasekaran Murugan Thilagaraj M Department of Computer Science and Engineering MVJ College of Engineering Bengaluru India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Industrial Internet of Things MVJ College of Engineering Bengaluru India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

An early-onset neurocognitive impairment, Alzheimer's disease (AD) affects individuals. The symptoms of this illness include diminished memory, strange conduct, and linguistic difficulties. Since AD is the source of 60-80% of instances of dementia, it is the single most prevalent cause of dementia. An AD that is neurodegenerative begins with moderate cognitive impairment (MCI) and progressively worsens. It damages brain tissue, impairs ability to think and remember, and makes it difficult to carry out basic duties. The development of more sophisticated therapies for AD depends on swift detection. For this reason, stopping and managing the progression of AD depends greatly on early detection. The primary goal is to create a comprehensive framework for medical image categorization according to different phases of Alzheimer's disease and early identification of the condition. This work employs a deep learning approach, more precisely convolutional neural networks (CNN). Simple CNN architectures that handle 2D and 3D structural brain magnetic resonance imaging (MRI) form the basis of the suggested methodology. The limited quantity of datasets presents the primary obstacles for medical images. Thus, in order to optimize the size of the dataset and avoid the issue of overfitting, methods to augment data are used. Techniques for resampling, such as downsampling and oversampling are employed to address imbalanced dataset collections.

关键词： Solid modeling Three-dimensional displays Parkinson's disease Magnetic resonance imaging Resonance Convolutional neural networks Proposals Alzheimer's disease Older adults Medical diagnostic imaging

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Design and Implementation of Flutter Based Multi-Platform Docker Controller App

Design and Implementation of Flutter Based Multi-Platform Do...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Saxena, Adarsh Singh, Sudhakar Prakash, Shiv Yadav, Nand Lal Yang, Tiansheng Rathore, Raikumar Singh Singh, Shreya University of Allahabad Department of Electronics and Communication Prayagraj India University of South Wales Faculty of Business and Creative Industries Pontypridd United Kingdom Cardiff School of Technologies Cardiff Metropolitan University Cardiff United Kingdom Institute of Engineering and Technology Department of Computer Science & Engineering Dr. A.P.J. Abdul Kalam Technical University Lucknow India

ISBN: (纸本)9798350369106

This paper focuses on developing a Flutter application for controlling Docker resources remotely. The application provides a user-friendly interface for executing various Docker-related commands on the server where the Docker engine is installed. The application uses the SSH protocol to establish a secure connection with the server and execute the commands. Further, an alternative approach is also explored, which involves connecting the application with the Docker engine using HTTP. This proposed Docker controller application provides a significant advantage for managing Docker resources remotely, which is highly beneficial in DevOps fields. It provides a user-friendly interface to manage containers, making it easy to create, start, stop, restart, and remove containers. It abstracts away the complexities of working with Docker commands, allowing users to interact with containers more intuitively. It can be used to manage a number of docker engines from one place making it easy to control and monitor all the docker resources. Its performance, security, and scalability are evaluated using various testing techniques, and the results are found satisfactory. Further improvements may include enhancing the application's features, optimizing the performance, and exploring other possible approaches for establishing the connection between the application and the Docker engine. © 2024 IEEE.

关键词： HTTP

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A Deep Analysis on Security Aspects of Cloud Computing Ontologies

A Deep Analysis on Security Aspects of Cloud Computing Ontol...

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Computing, Power and communication Technologies (IC2PCT), IEEE International Conference on

作者： Girish Singh Bisht N Gobi Vinod Mansiram Kapse Department of Computer Science and Engineering Tula’s Institute Dehradun India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology Greater Noida Uttar Pradesh India

The primary objective of this research is to get a comprehensive understanding of Cloud Computing ontologies, their applications, and their specific areas of interest. The proposed research methodology for this systematic review comprises a well-defined research topic, criteria for including and excluding studies, and a comprehensive search strategy to identify relevant source papers for the review. The chosen research is categorized into four main areas: elucidation of cloud resources and services, elaboration of cloud security, clarity of cloud interoperability, and understanding of cloud services discovery and selection. Utilizing ontologies to improve the security and smooth integration of cloud computing services uncovers various challenges and identifies areas for more research.

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AI-Driven Fault Detection in Smart Grids

AI-Driven Fault Detection in Smart Grids

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Electrical Power engineering, communication and Computing Technology(ODICON), Odisha International Conference on

作者： S. Pradeep C. R. Edwin Selva Rex D. Kalaiyarasi Ramya Dhandapani M. Sakthivel K. Vijaipriya Department of Electronics and Communication Engineering S. A. Engineering College Tamil Nadu India Department of Electrical and Electronic Engineering Vignana Bharathi Institute of Technology Hyderabad Telangana India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Madurai Tamil Nadu India Department of Electronics and Communication Engineering Sri Sai Ranganathan Engineering College Coimbatore Tamil Nadu India

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

The use of artificial intelligence (AI) for fault finding in smart grids, with the end goal of improving the reliability and efficiency of ultramodern power systems. Smart grids, which are distinguished by their intricate architectural design and massive data collection, are equipped with cutting-edge approaches for the quick and precise identification of breakdowns. Through the utilization of artificial intelligence techniques, such as Deep Reinforcement Learning (DRL) method can analyze big datasets, identify anomalies, and accurately predict implicit errors with a high degree of precision. In this research, artificial intelligence algorithms that are colorful are investigated, along with their effectiveness in real-time monitoring and defect finding within smart grids. The advantages of AI-driven defect discovery over traditional methods are demonstrated through case studies and simulations. These advantages include important breakthroughs in response time, delicacy, and functional adaptability. The incorporation of artificial intelligence into the management of smart grids holds the potential to bring about substantial breakthroughs in the areas of grid stability maintenance, time-out reduction, and energy distribution optimization. The purpose of this research is to provide a complete analysis of the role that artificial intelligence plays in defect finding. It also offers insight into future advances and practical applications in smart grid systems.

关键词： Power engineering Power system stability Prediction algorithms Stability analysis Real-time systems Smart grids Power system reliability Time factors Artificial intelligence Optimization

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

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

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communication, Computing and Industry 4.0 (C2I4), International Conference on

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

ISBN: (数字)9798350379884

ISBN: (纸本)9798350379891

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.

关键词： Ant colony optimization Hospitals Vehicular ad hoc networks Maintenance engineering Routing Throughput Routing protocols Biology Vehicle dynamics Convergence

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Implementation of the Optical Lock through Visible Light communication using AES 1

Implementation of the Optical Lock through Visible Light Com...

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1st IEEE International Conference on Advances in Signal Processing, Power, communication, and Computing, ASPCC 2024

作者： Swain, K.P. Mohapatra, Sumant Kumar Mallik, Puspanjali Dash, R.K. Lenka, Rakesh Kumar Trident Academy of Technology Department of Etc Bhubaneswar India Shailabala Women's College Dept. of Cse Cuttack India Galgotia University Dept. of Electrical Electronics and Communication Engineering India Central University of Odisha Dept. of Computer Science Koraput Sunabeda India

ISBN: (纸本)9798350355093

This study presents the development and implementation of a novel optical lock using visible light communication (VLC), leveraging a custom-built mobile application and flash LED for secure information transmission. The system utilizes an Android application with a user-friendly interface to input and encrypt messages using the AES-128 bit algorithm and Base64 encoding. The encrypted message is transmitted via a smartphone's flash LED as an OOK signal, received by a specially designed PCB composed of a photodiode and a servo motor. At the heart of the receiving mechanism is a Raspberry Pi, which is responsible for decrypting the message and executing a comparison check with a pre-stored message to determine system unlocking. This innovative approach combines the convenience of mobile technology with advanced encryption, offering a robust solution in optical locking systems. © 2024 IEEE.

关键词： Locks (fasteners)

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Connectivity Disturbances in Self-Limited Epilepsy with Centrotemporal Spikes: A Partial Directed Coherence Analysis of Electroencephalogram

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Clinical EEG and Neuroscience 2024年第2期55卷 257-264页

作者： Chiang, Ching-Tai Yang, Rei-Cheng Kao, Yu-Chia Wu, Rong-Ching Ouyang, Chen-Sen Lin, Lung-Chang Department of Computer and Communication National Pingtung University Pingtung Taiwan Departments of Pediatrics Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan Departments of Pediatrics E-Da Hospital Kaohsiung Taiwan Department of Electrical Engineering I-Shou University Kaohsiung Taiwan Department of Information Management National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Pediatrics School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan

Although the remission of self-limited epilepsy with centrotemporal spikes (SeLECTS) usually occurs by adolescence, deficits in cognition and behavior are not uncommon. Several functional magnetic resonance imaging (fMRI) studies have revealed connectivity disturbances in patients with SeLECTS associated with cognitive impairment. However, the disadvantages of fMRI are expensive, time-consuming, and motion sensitive. In the current study, we used a partial directed coherence (PDC) method to analyze electroencephalogram (EEG) for exploring brain connectivity in patients with SeLECTS. This study enrolled 38 participants (19 patients with SeLECTS and 19 healthy controls) for PDC analysis. Our results demonstrated that the controls had significantly higher PDC inflow connectivity in the F7, T3, FP1, and F8 channels than patients with SeLECTS. By contrast, the patients with SeLECTS demonstrated significantly higher PDC inflow connectivity than did the controls in the T5, Pz, and P4 channels. We also compared the PDC connectivity in different Brodmann areas between the patients with SeLECTS and the controls. The results revealed that the inflow connectivity in the BA9_46_L area was significantly higher in the controls than in the patients with SeLECTS, whereas the inflow connectivity in the MIF_L area 4 was significantly higher in the patients with SeLECTS than in the controls. Our proposed approach of combining EEG with PDC provides a convenient and useful tool for investigating functional connectivity in patients with SeLECTS. This approach is time-saving and inexpensive compared with fMRI, but it achieves similar results to fMRI. © EEG and Clinical Neuroscience Society (ECNS) 2023.

关键词： cognition EEG fMRI partial directed coherence self-limited epilepsy with centrotemporal spikes

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Advancing Parkinson's Detection Through Intelligent Algorithms

Advancing Parkinson's Detection Through Intelligent Algorith...

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International Conference on Advanced Computing and communication Systems (ICACCS)

作者： A. Rasheedha Lynsha Helena Pratheeba HP S. Caroline Vinnetia S. Sharmila Devi A. Reethika S. Gunanandhini Department of Biomedical Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Computer Science and Engineering CMR Institute of Technology Bangaluru India Department of Biomedical Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Electronics and Communication Engineering Dr.N.G.P.Institute of Technology Coimbatore India Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Parkinson's disease (PD) is a slowly developing nervous disorder that presents with signs like those of other illnesses. Appropriate diagnosis and therapy depend on early detection. One common method for identifying and evaluating Parkinson's disease is handwritten notes. Early Parkinson's disease (PD) detection has been studied using a number of machine learning techniques, although the most have poor performance accuracy. An effective deep learning model is suggested to help diagnose Parkinson's disease early. The model uses evolutionary algorithms, such as the K-Nearest Neighbour approach, to pick the most optimal features for high-performance accuracies. The model obtains an zone under curve of 0.92, detection accuracy of over 96%, and precision of 99%. To illustrate the effectiveness of the model, its performance is contrasted with cutting-edge machine learning and deep learning-based PD detection methods.

关键词： Training Accuracy Systematics Machine learning algorithms Parkinson's disease Transfer learning Medical treatment Evolutionary computation Standards Genetic algorithms

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