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The intelligent optimization of AI/ML framework to Detect Network Abnormalities via Time Series Analysis 15

The intelligent optimization of AI/ML framework to Detect Ne...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Rongali, Aruna Sri Jagannayaki, K. Sarkar, Paramita Dhole, Sumedh Vithalrao Kumar, Ch. Mohan Sai Matad, Nanditha S Ph.D University of The Cumberlands Emphasis/Information Technology Information Technology United States Institute of Aeronautical Engineering Hyderabad India JIS University Department of Computer Science and Engineering Kolkata India College of Engineering Pune Department of Electronics & Communication Engineering Pune-satara road Maharashtra Dhankawadi Pune411043 India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering India New Horizon college Department of Mathematics Bangalore India

ISBN: (纸本)9798350370249

This paper presents an intelligent optimization of the AI/ML framework to detect network anomalies via time collection analysis. The proposed framework entails leveraging an AI / ML version to manner the time series facts and to identify community anomalies. Traditional methods of describing the information through time variety or histogram analysis are then improved by introducing a supervised schooling set of rules for higher accuracy. The methodology is similarly validated using a real-global dataset. The result of the preliminary analysis famous that the AI / ML model trained with the proposed framework enables efficient detection of extraordinary network sports. The findings of this examination underscore the benefits of using AI/ML models for the timely detection of community anomalies. The painting affords an intelligent optimization of AI/ML frameworks to locate community abnormalities via time collection evaluation. The proposed framework is evolved through leveraging a hard and fast of features, consisting of topology, via graph-primarily based strategies, an excellent way to seize the worldwide behavior of the community and trigger alarms in case of ability network anomalies. The time series evaluation is then achieved to extract and model features from the monitored traffic information to discover patterns and insights, also used in labeling and predicting a suitable elegance for each traffic file. In the end, the AI/ML model skilled the use of the categorized statistics to locate any ability community abnormalities and cause alarms to inform of such unusual behavior. The proposed framework, as a result, presents an automated solution for network anomaly detection and acts as a powerful tool in mitigating security threats for businesses and companies that rely heavily on their community infrastructure... © 2024 IEEE.

关键词： Time series

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A Well Structure for High Voltage DC Architectures for the Purpose of Aircraft: A Deep Comparative Study for Different Design Options 1

A Well Structure for High Voltage DC Architectures for the P...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Balguri, Praveen Kumar Gupta, Saket Socsa, Shruti Aggarwal Ravivarman Mukuntharaj, C. Kshirsagar, Pranali S. Institute of Aeronautical Engineering Department of Aeronautical Engineering Hyderabad India Tmu Department of Ee Chennai India Iimt University Meerut India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore641032 India Vishwakarma Institute of Information Technology Department of Computer Pune India

ISBN: (纸本)9798350384246

The incorporation of a high-voltage direct current into the internal battery system is the topic of this article. The voltage supply of traditional airplane networks is 115 V AC (star electricity), with a changeable bandwidth of 360-800 Hz. To reduce wire strength, more recent airplanes attempt to incorporate 230 V AC units into the system. Prospective flight may incorporate lines thanks to the 'More Power generation Aviation' idea. The terminal facilities must be taken into account in all ideas. Handheld Surface Power Devices or a converter powered by the air transportation serve as the foundation for the surface power source. The earth source provides 115 V AC at a full capacity of 90 kVA that for each unit and just a set bandwidth of 400 Hertz. A switch to a - system has many benefits. Lesser wire muscle mass results from divine creator. The photo voltaic design can also be improved. When using a DC source, it is possible to build loads with much smaller circuits in there. This essay contrasts various HVDC incorporation options. The possibility of a base source of energy using measures are applied and additional circumstances like fiber-glass composite thermoplastic airframes are contemplated. An effective interior electricity source is the main emphasis. However, the complete internal available power system is examined in order to reach a broad conclusion. © 2024 IEEE.

关键词： Wire

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Deep Learning Approaches for Web Page Classification in Focused Crawling 3

Deep Learning Approaches for Web Page Classification in Focu...

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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Dhabliya, Dharmesh Al-Fatlawy, Mohammed Hussien Kumhar, Bhupchand Rajeswari, M. Sundaram, N. Kalyana Vishwakarma Institute of Information Technology Department of Information Technology Pune India College of Technical Engineering The Islamic University Najaf Iraq Ies University Department of Computer Science & Engineering Bhopal India Prince Shri Venkateshwara Padmavathy Engineering College Electronics and Communication Engineering Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798350342338

Web page classification plays a crucial role in focused crawling, where the goal is to selectively retrieve relevant pages based on specific topics or domains of interest. Deep learning approaches have gained significant attention in recent years due to their ability to extract high-level representations from raw data. This paper presents an overview of deep learning techniques used for web page classification in focused crawling. We discuss various deep learning architectures, training strategies, and evaluation metrics employed in this context. Furthermore, we provide insights into the challenges and future directions in this rapidly evolving field. © 2023 IEEE.

关键词： Deep learning focused crawling natural language processing neural networks web page classification

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Implementation and deployment of the integration of IoT devices in the development of GIS in the cloud platform

Implementation and deployment of the integration of IoT devi...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Krishna Kumari, R. Gireesh, N. Bhavani, R. Sathish, K. Senthil, G.A. Praveena, S. Department of Mathematics Panimalar Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Andhra Pradesh Tirupati India Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Computer Science and Engineering Madanapalle Institute of Technology and Science Andhra Pradesh Madanapalle India Department of Information Technology Agni College of Technology Tamil Nadu Chennai India Department of Electronics and Communication Engineering Mahatma Gandhi Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9781032669663

The technological infrastructure provided by cloud providers, and used as a deployment platform, allows GIS to take advantage of its high storage and processing capacities;while the use of IoT devices allows the automation of the data collection process. However, the current implementations are carried out in an ad hoc manner, without providing solutions that facilitate their design, implementation and reuse. In this work, we propose a development approach that, based on models that describe at a high level of abstraction, both the system architecture and the interaction between its services and IoT devices, allows guiding the implementation and deployment activities in cloud environments. This has been illustrated with the design and implementation of a GIS application analyse the collected spatial data with air quality sensors and were deployed on the Google Cloud platform. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Air quality

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Deep Learning based vehicle image detection using Yolo V5 with Region-Based Convolutional Neural Network 3

Deep Learning based vehicle image detection using Yolo V5 wi...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Rani, S. Swapna Mudigonda, Aditya Hemanth, S.V. Sundararajan Reddy, G. Vinoda Amirthayogam, G. Engineering College Department of Electronics and Communication Engineering Hyderabad India JNIAS School of Planning and Architecture Telangana Hyderabad500034 India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana Hyderabad501401 India PSNA College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India Telangana Hyderabad501401 India Hindustan Institute of Technology and Science Department of Information Technology Tamilnadu Chennai603103 India

ISBN: (纸本)9798350372120

Detecting and classifying vehicles is a modern technology with numerous uses. Administration and regulation of traffic is one of the primary uses. Projects utilizing image processing to prevent traffic accidents heavily rely on vehicle monitoring and detection. Monitoring and recording human movement in surveillance situations require the ability to follow moving objects. Considering its significance, it provides a valuable image processing-based vehicle detection technique - a vehicle tracking and detection system based on images. The recently released high-resolution road vehicle dataset supports deep learning-based vehicle detection and monitoring on the Python platform. It includes over 100 well-defined pictures taken from movies from various locations. This data is produced using a range of image processing methods. The most recent iteration of the YOLO model, the v5 model, was employed for detection. R-CNN is additionally utilized for model detection. The four stages in the Region-Based Convolutional Neural Network (RCNN) process are preprocessing, segmentation, feature selection, and classification. The first step is identifying moving vehicles accurately. Preprocessing the original image is necessary. This enhances the image's quality, valuable information extracted, or unwanted areas cropped out. Segmentation is the second step. The region's borders are established, and the entire image is divided into numerous smaller areas using a threshold. The vehicle image's most pertinent aspects or characteristics (features) that aid in precise detection are found in the third stage, feature selection. A region-based convolutional neural network (RCNN) is employed to determine the categorization. The Yolov5 method can be used to identify and categorize items. The Region-based Convolutional Neural Network (RCNN) approach enhances accuracy and temporal complexity over current segmentation algorithms. © 2024 IEEE.

关键词： Vehicles

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Application of Evolutionary Computing for Optimal Design of Base Station Antenna 7

Application of Evolutionary Computing for Optimal Design of ...

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7th IEEE International Conference on Electronic information and communication Technology, ICEICT 2024

作者： Khan, Naveed Hamid Nawaz Feng, Botao Ding, Xiao Deng, Li Gu, Wenzhe Chung, Kwok L. Shenzhen University College of Physics and Optoelectronic Engineering Shenzhen518060 China Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Macau University of Science and Technology Faculty of Humanities and Arts Macau999078 China Beijing University of Posts and Telecommunications College of Information and Communication Engineering Beijing China Huizhou University School of Computer Science and Engineering Huizhou China Huizhou University School of Computer Science and Engineering Huizhou516007 China

ISBN: (纸本)9798350384437

This study outlines the development and refinement of an innovative base station antenna for contemporary wireless communication systems, which operates within the frequency range of 1.9 to 4.9 GHz. The antenna is made of copper and has a radiating patch thickness of 0.2 mm. The ground patch measures 38×38 mm and the total height of the antenna is 16 mm. The performance of the system relies on four reflecting walls that have been carefully carved to reduce out-of-band radiations and minimize interference. The antenna attains a peak gain of 6 dBi throughout the operational range, while keeping the |S11| parameters below -10 dBi, ensuring effective signal transmission. The process of optimization involves the utilization of evolutionary computers, more specifically a Genetic Algorithm, to meticulously adjust the design parameters. This approach guarantees that the antenna fulfills the demanding criteria of high gain and wide bandwidth, offering a strong and reliable solution for modern communication requirements. © 2024 IEEE.

关键词： Antenna grounds

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Deep Learning Framework for Early Detection of Heart Attack Risk and Cardiovascular Conditions using Retinal Images 1

Deep Learning Framework for Early Detection of Heart Attack ...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Sakthidevi, I. Srinivasan, A. Gayathri, C. Julie, E. Golden Krishnan, R. Santhana Darney, P. Ebby Adhiyamaan College of Engineering Department of Information Technology Hosur India Velammal College of Engineering and Technology Department of Information Technology Madurai India Psna College of Engineering and Technology Department of Information Technology Dindigul India Anna University Regional Campus Department of Computer Science and Engineering Tirunelveli India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India RajaRajeswari College of Engineering Department of Electrical and Electronics Engineering Bangalore India

ISBN: (纸本)9798350359756

In today's world, cardiovascular diseases including heart attacks are the prime reason for mortality. Detecting heart attack risk at early stages and accurate assessment of such risks related to cardiovascular conditions play an essential role in active patient management and preventive interventions. In the current research work, a novel deep learning framework called "CardioSightFrame"is proposed for detecting heart attack risks and cardiovascular conditions at early stages using retinal images. The proposed algorithm integrates Graph Convolutional Networks (GCNs) and Vision Transformers (ViTs) to control both local structural information and global contextual understanding from the retinal scans. A comprehensive simulation analysis is conducted to evaluate the performance of CardioSightFrame and it is compared with the existing algorithms. Also, the predictive accuracy of the proposed algorithm is measured using appropriate simulation metrics. By treating retinal images as graph nodes and leveraging self-attention mechanisms, the proposed algorithm achieves accurate and efficient feature extraction which is vital for early detection of heart attack risk and cardiovascular conditions. To evaluate CardioSightFrame's performance, wide-ranging simulation experiments are conducted using a varied dataset of retinal images. Through these comparisons, the predictive accuracy, sensitivity, specificity, and computational efficiency of CardioSightFrame are established. The simulation results indicate that CardioSightFrame outclasses existing algorithms in terms of accuracy and sensitivity for early detection of heart attack risk and cardiovascular conditions. The algorithm's ability to efficiently investigate retinal images with reduced computational overhead further enhances its applied pertinence. © 2023 IEEE.

关键词： Image enhancement

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A Novel Optimized AI Based Model for Traffic Prediction in VANET 5

A Novel Optimized AI Based Model for Traffic Prediction in V...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Pradeep, R. Revathi, K. Thomas, Aby K Srikanth, R. Sns College of Engineering Department of Electrical and Electronics Engineering Coimbatore India Sns College of Engineering Department of Information Technology Coimbatore India Alliance College of Engineering and Design Alliance University Department of Electronics and Communication Engineering Bangalore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350321425

Research into a novel type of wireless network, the vehicle ad hoc network (VANET), has gained an increasing research interest in recent years. By exchanging data about things like vehicle velocity, location, and direction, VANET facilitates interaction between moving vehicles and fixed infrastructure. It's best to take an alternate route, if possible, when there's a high probability that a large number of cars will use the same one to get from point A to point B. If vehicles could precisely foresee traffic congestion, they could potentially evade it. Hence, suggested using DRL in VANET to improve traffic congestion forecasting. In addition, this deep reinforcement learning model examines and compares various neural networks, such as the CNN, MLP, and LSTM, to determine which is best with the results. The proposed strategy is evaluated based on the ATTD and the AWTD of vehicles, which serve as metrics to assess its effectiveness. Through simulation analysis, it is evident that the suggested approach enhances the average travel time delay and average waiting time delay across multiple iterations, considering the environmental factors as inputs. The simulation results demonstrate that compared to existing algorithms used for network traffic prediction, the deep reinforcement learning model provides superior performance in terms of both execution time and prediction errors. © 2023 IEEE.

关键词： Traffic congestion

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Calculation of The Firefly Method's Parameters for PID Parameters in Applications Involving Solar Panels

Calculation of The Firefly Method's Parameters for PID Param...

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2024 International Conference on Sustainable Computing and Integrated communication in Changing Landscape of AI, ICSCAI 2024

作者： Kavitha, M.S. Verma, Pradeep Kumar Prasath, S. Ram Bhatt, Rahul Amudha, A. Marathe, Vijay University R.M.K. Engineering College Kavaraipettai Chennai India Department of EE TMU India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore India School of Engineering and Computer Dev Bhoomi Uttarakhand University India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore India Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350355338

To generate the most electrical power, the sections panels must operate at their best. A sunlight tracker may assist with the panels function at their best. details solar cell tracker operates by tracing the path of sunlight. The efficiency the sunlight energy tracker was optimized using some Proportional, Integral, It (PID) centered command. To obtain their PID variable, an ideal adjusting is required. A clever technique for optimizing PID settings involves the Fireflies technique. The system makes use of the following three Fireflies An algorithm (FA) variables: Firefly accelerate is determined by beta, motion flexibility by alpha, while more intricate limitations or limitations are determined by gamma. issues. details beta appreciate drive is used for this multifaceted center solar power monitoring study, altering their beta appreciate about 0.1 to 0.9. subsequently was discovered about the outcomes of ten models how their PID unchanged remained varied. The ideal outcomes on known as horizon occur when their beta has been set during 0.4, while the most adverse outcomes occur when their beta is provided at 0.8. details Beta value of 0.3 produces the ideal results upon known as vertical plane, while 0.8 produces lowest results. © 2024 IEEE.

关键词： Solar power generation

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Advancements in Multi-Cloud Applications for Enhanced E-Healthcare Services 1

Advancements in Multi-Cloud Applications for Enhanced E-Heal...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Naidu, P Ramesh Gowda, Dankan Sarma, Parismita Arora, Dinesh Suneetha, Sampathirao Patil, Sandip Ramesh Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bangalore India Bms Institute of Technology and Management Department of Electronics & Communication Engineering Karnataka Bangalore India Gauhati University Assam Department of Information Technology Assam Guwahati India Chandigarh Engineering College Department of Electronics and Communication Punjab Mohali India Andhra University Department of Computer Science & Systems Engineering Andhra Pradesh Visakhapatnam India Bharati Vidyapeeth College of Engineering Department of Communication Skill Navi Mumbai India

ISBN: (纸本)9798350330915

E-Health Record Security Research on a Cloud-Based Multi-Layer Framework reaches its climax in a string of noteworthy discoveries, demonstrating how the framework may transform cloud health data security. The framework's multi-tiered design proved to be an effective barrier against a wide range of cyber threats, protecting the privacy and security of patients' medical records. An important factor in the varied healthcare industry is the fact that it can be easily scaled and adjusted to meet the needs of healthcare providers of varied sizes and capabilities. Nevertheless, there are obstacles that need to be addressed, according to the report. These include the difficulty of implementation and the ongoing need for upgrades and modifications to address changing cyber threats and technical advances. Healthcare firms must continuously implement educational programs due to the reliance on user compliance and training. With an eye toward the future, this structure lays the groundwork for more sophisticated studies in the subject. Potential research directions include improving cross-platform compatibility, optimizing resource utilization to reduce performance implications, and integrating AI and ML for automated threat response and predictive analytics. © 2023 IEEE.

关键词： Cloud Computing Data Security Healthcare Optimization Multi-Cloud Applications Patient Data Privacy Scalability

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