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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Smitha, G.V. Pavithra, B. Noushad, Izin Joshi, Rahul Vats, Ujjwal Dayananda Sagar College of Engineering Affiliated to Vtu Department of Masters in Computer Applications Karnataka Bangalore560078 India

ISBN: (纸本)9798350315905

Cloud services have become an essential part of modern computing, providing on-demand access to computing resources and services over the internet. However, ensuring the reliability and performance of cloud services is a challenging task, as these services are highly complex and dynamic. Traditional monitoring techniques, such as threshold-based monitoring, are not suitable for cloud services, as they cannot handle the dynamic nature of these services.A stratified approach to monitoring cloud services using machine learning is a novel approach that uses machine learning algorithms to monitor cloud services in a more effective way. This approach is based on the principle of stratification, which involves dividing a complex system into several layers or strata, each with its own monitoring *** first layer of the stratified approach involves monitoring the infrastructure layer, which includes infrastructure, both real and digital used to provide Cloud computing. This layer is monitored using machine learning algorithms that can detect anomalies in the network traffic, CPU usage, memory usage, and other infrastructure-related *** second layer involves monitoring the platform layer, which includes the software platforms and frameworks used to develop and deploy cloud services. This layer is monitored using machine learning algorithms that can detect anomalies in the application logs, database usage, and other platform-related *** third layer involves monitoring the application layer, which includes the actual cloud services provided to users. This layer is monitored using machine learning algorithms that can detect anomalies in the user requests, response times, and other application-related *** using machine learning algorithms to monitor cloud services at different layers, the stratified approach can provide a more comprehensive and effective monitoring solution than traditional monitoring techniques. The approach can also adapt to the d

关键词： Machine learning

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Oral Cancer Classification Using GLRLM Combined with Fuzzy Cognitive Map and Support Vector Machines from Dental Radiograph Images 1

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1st International Conference on Intelligent Computing for Sustainable Development, ICICSD 2023

作者： Anuradha, K. Fathima, H. Kavithamani, K. Uma, K.P. School of Computer Applications Karpagam College of Engineering Tamil Nadu Othakkal Mandapam India Department of Science and Humanities Hindusthan College of Engineering and Technology Tamil Nadu Coimbatore India

ISBN: (数字)9783031612985

ISBN: (纸本)9783031612978

In recent years, Fuzzy Cognitive Maps (FCM) are widely used in medical decision schemes. According to data, oral cancer is the fifth most common cancer in India, with a higher occurrence among men than women. In order to save lives, correct diagnosis and prompt treatment is required. When oral cancer is identified early, the 5-year survival rate exceeds 80%, but advanced stages of the illness have 5-year survival rates that are less than 20–30%. But, the majority of cases are detected in advanced stages where treatment becomes unsuccessful. To achieve a better classification of oral cancers, Gray Level Run Length Matrix (GLRLM) is combined with Fuzzy Cognitive Map and Support Vector Machines. In this work, fifty Dental Radiograph images are used for cancer classification. Initially, Linear contrast stretching algorithm is employed to remove the noise present in the image. The filtered picture is split to create a Region of Interest (ROI). The ROI picture is used to extract the GLRLM features. The classification of cancer is based on the features extracted. From the extracted features, a fuzzy cognitive map is employed. Cancer categorization is carried through Machine Learning approaches. Measures like sensitivity, specificity, precision, accuracy, and F-score have been used to describe the performance of the SVM classifier. The study’s findings demonstrate that GLRLM-based features are strong discriminating features for statistical analysis of Dental Radiograph images and can be helpful in the identification of cancer. By combining the texture features based on GLRLM and FCM, the accuracy (94%) as a whole is improved. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Support vector machines

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A Study on Swarm-Based Approaches for Intrusion Detection System in Cloud Environment

A Study on Swarm-Based Approaches for Intrusion Detection Sy...

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International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022

作者： Nishika Solanki, Kamna Dalal, Sandeep University Institute of Engineering and Technology MDU Rohtak India Department of Computer Science and Applications MDU Rohtak India

ISBN: (纸本)9789819909803

Cloud is an advanced Internet of Things enabled network. The layered and distributed nature of this network opens this network to the public environment and varied kind of users. Bulk of data is stored on centralized system, and large amount of information is communicated in the real environment. This public and large amount of shared data and communication opens the network for intruders. Various intrusion detection systems were designed and implemented on different layers of the cloud network to detect and prevent different kinds of internal and external attacks. In the recent years, various swarm-based methods were integrated within intrusion detection system to optimize the performance. In this paper, a detailed study and review of cloud- and Internet of Things-based intrusion detection systems is presented. The analytical and descriptive study of swarm-based intrusion detection system is provided in this paper. This study identifies that the swarm integration within intrusion detection and classification methods improved the performance and reliability of conventional systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Intrusion detection

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Analysing weather conditions using Firebase

Analysing weather conditions using Firebase

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2023 International Conference on Green Energy, Computing and Intelligent Technology, GEn-CITy 2023

作者： Jha, Abhishek Lalan Ashraf, Azhar Verma, Sahil Kaur, Maninder Computer Science & Engineering Chandigarh University Gharuan India Department of Computer Science and Engineering Uttaranchal University Dehradun India Department of Computer Science and Applications Guru Gobind Singh College for Women Chandigarh India

ISBN: (纸本)9781839539596

Forecasting Weather to a specific time and location via the use of science and technology for its specific location from the 19th century, millions of humans have started predicting weather forecasting. This technology uses meteorology so that it predicts the weather for the future so that the response of the atmosphere can be recorded for the future purpose. Technology for predicting weather we use barometric pressure, in that which show the current weather condition and cloud cover for Sky condition this prediction model is based on the computer Based model show the records of a wide range of atmospheric will be recorded by having this technology for model prediction is pattern recognition, telecommunication model, performance knowledge, but its skill required the more in this technology for forecasting the chaotic character of the weather which is overpowered by the measurement error by this less accuracy the atmospheric dynamic model field to predicting the model which is already exist for forecasting. As a result, as the time gap between today and the predicted model for the forecast has a time gap so it leads to less accuracy, the range of forecasting prediction for selecting the most likely and accurate outcome in the model contest aid narrows free error in it. The most difficult challenge in the world is to predict the forecasting using the Meteorology virtual application for the most significant difficult Technology. In this paper, we are going to investigate data mining techniques and artificial neural networks, my research paper presents the service-oriented model that the architecture for the weather information system will be linked with the GPS system. Algorithms such as Artificial neural networks and Decision tree algorithms are going to use for prediction model for forecasting by gathering information on meteorological data collection of specific time can use for achieving accuracy therefore, this paper includes of classification of different rules f

关键词： Weather forecasting

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Detection and Classification of Skin Cancer Using Back Propagation Ann 6th

Detection and Classification of Skin Cancer Using Back Propa...

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6th International Conference on Communications and Cyber-Physical engineering, ICCCE 2023

作者： Murali Mohan, S. Murali Mohan Kumar, A. Vennela, G. Venugopal Reddy, R. Department of ECE Mother Theresa Institute of Engineering and Technology AP Palamaner India Mother Theresa Institute of Computer Applications AP Palamaner India

ISBN: (纸本)9789819971367

Cancer of the skin is the most common form of the disease and is to blame for the deaths of millions of people each year. The early detection of potentially hazardous skin cancer cases and the administration of suitable treatments are essential components in assuring a relatively low overall death rate while maintaining a very high percentage of those that survive. A significant portion of the relevant studies concentrate on algorithms that are relied on machine learning, however these algorithms are unable to deliver the highest possible level of accuracy and specificity. At the preprocessing step, enhancing procedures including sharpening filters and smoothing filters are employed to reduce noise from the image. After that, Ostu segmentation was used for accurate diagnosis of the cancerous area of the skin. A Back-Propagation Artificial Neural Network (BP-ANN) was constructed with relation to the categorization of skin cancer using the Spatially Grey Level Dependency matrix (SGLD) characteristics, in order to capture the maximum efficiency of the system in the proposal. As a result, the findings of the proposal may be efficiently used to the categorization of both benign and malignant forms of skin cancer. Comparing the suggested technique to the state of the art methodologies, it has been shown via the modelling exercise that the proposed algorithm yields superior results qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neural networks

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Analytical Evaluation of Web Performance Testing Tools: Apache JMeter and SoapUI 12

Analytical Evaluation of Web Performance Testing Tools: Apac...

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12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Tiwari, Vatsya Upadhyay, Sachin Goswami, Jayati Krishna Agrawal, Sanjiv Bsa College of Engineering & Technology Department of Computer Science & Applications Mathura India GLA Univ. Deparmtnent of Computer Engineering & Application Mathura India G L Bajaj Group of Institutions Computer Sciecnce & Engineering Mathura India

ISBN: (纸本)9781665462617

Computing on the Web is based on distributed system applications that use the client-server architecture. Since web-based applications have been beta-tested, the quality criteria cannot be fixed. Whenever a website is launched, the question is always raised as to how many HTTP requests of users can the website handle at once. As part of this paper, various tools for performance testing will be used, including Apache SoapUI, JMeter, etc., and selection tools for load testing will be discussed. © 2023 IEEE.

关键词： Load testing

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Comprehensive Survey on Internet of Medical Things (IoMT) - applications and Challenges 15

Comprehensive Survey on Internet of Medical Things (IoMT) - ...

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

作者： Anusha, V. Sai Kumar, R. Kiran Kumar, G. Charan Mabjan, Palagiri Madanapalle Institute of Technology & Science Department of Electronics and Communication Engineering Madanapalle517325 India Annamacharya University Department of Computer Applications Rajampet India

ISBN: (纸本)9798350370249

The Internet of Medical Things (IoMT) is changing healthcare through the use of the Internet to connect medical devices, apps, and systems. This helps improve patient care, makes healthcare more efficient, and improves health outcomes. This paper deals with the edge-fog-cloud computing architectures for Internet of Health applications. Remote patient monitoring is done by collecting the data from wearable health devices, processing at different communication levels, applying intelligent algorithms and providing insights to the patient and healthcare professionals. This paper reviews the IoMT architecture and how the entire workflow is managed at device, edge, fog, and cloud levels. The paper also discusses IoMT challenges, such as data security and privacy concerns due to the interconnectedness and interoperability of devices at each layer and hurdles for seamless data integration within healthcare networks. © 2024 IEEE.

关键词： Electronic health record

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Deep Reinforcement Learning Based Secure Transmission for UAV-Assisted Mobile Edge Computing

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第17期18卷 154-169页

作者： Vijayalakshmi, N. Gulati, Sagar Sujin, B. Ben Rao, B. Madhav Kumar, K. Kiran Department of Computer Science and Applications SRMIST Ramapuram Campus Tamil Nadu Chennai India School of Computer Science and IT JAIN Deemed to be University Karnataka Bengaluru India Computer Engineering Department University of Technology and Applied Sciences Nizwa Oman Department of CSE SIR C R Reddy College of Engineering Andhra Pradesh Eluru India Department of CSE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

The increasing computational demand for real-time mobile applications has led to the development of mobile edge computing (MEC), with support from unmanned aerial vehicles (UAVs), as a promising paradigm for constructing high-throughput line-of-sight links for ground users and pushing computational resources to network edges. Users can reduce processing latency and the load on their local computers by delegating tasks to the UAV in its role as an edge server. The coverage capacity of a single UAV is, however, very limited. Moreover, it will be easy to intercept the data that is transferred to the unmanned aerial vehicle. Thus, for UAV-assisted mobile edge computing, we proposed a transmission technique based on multi-agent deep reinforcement learning in this study. The recommended approach to maximize UAV deployment first applies the particle swarm optimization algorithm. Then, deep reinforcement learning is utilized to optimize the secure offloading to maximize the system utility and minimize the quantity of information eavesdropping, taking into consideration different user task types with diverse preferences for processing time and residual energy of computing equipment. The results of the simulation demonstrate that, in comparison to the single-agent strategy and the benchmark, the multi-agent approach can optimize offloading more successfully and produce higher system utility. © 2024 by the authors of this article.

关键词： Deep reinforcement learning

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Artificial Intelligence-based Solution for the Prediction of Power Consumption in Electronics and Software applications

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IETE Journal of Research 2024年第1期70卷 356-371页

作者： C, Savitha Khampariya, Prabodh Singh, Kamred Udham Kumar, Ankit Singh, Teekam Swarup, Chetan Department of Electronics and Communication Engineering Sri Satya Sai University of Technology & Medical Sciences Sehore India Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Computer Engineering and Applications GLA University Mathura281 406 India School of Computer Science University of Petroleum and Energy Studies Dehradun India Basic Science College of Science and Theoretical Studies Saudi Electronic University Riyadh-Male Campus Riyadh11673 Saudi Arabia

As new computing paradigms such as mobile grids and clouds become more commonplace, mobile devices are becoming increasingly attractive to scientists and HPC users who need high-performance computing capabilities. There are still many challenges to design software that uses mobile devices, such as their restricted capabilities compared to traditional devices such as PCs and servers. It’s even more critical to remember that batteries are the primary power source for mobile devices. Thus people delete programmes that severely reduce their battery life. The upshot is that it is common for developers to have no idea how much power specific hardware and software components use. So even if they have been trained in software development, they still require specific standards and skills to design energy-efficient apps. This Paper investigates ways to minimize mobile device energy usage by restructuring source code. However, we did not overlook the prospect of applying our findings to other domains, such as recurring demanding computational cores in scientific applications. Specifically, this research contributes to the field of Green Computing and mobile software development. © 2024 IETE.

关键词： Energy efficiency

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Revolutionizing Facial Feature Extraction with AI: An Innovative Approach

Revolutionizing Facial Feature Extraction with AI: An Innova...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： B. Lakshmi Jandhyam Lakshmi Alekhya K Parish Venkata Kumar B. Kishore K. Kamala Nyni K. Likhita Dept. of Computer Applications V.R. Siddhartha Engineering Dept of computer science and engineering V.R. Siddhartha Engineering College Department of Computer Applications V.R. Siddhartha Engineering College Department of Computer Applications

This paper provides a technique for detecting human faces & extracting their features based on segmentation of skin tone & facial structure. The system goes through several stages. The method first searches for regions where faces might be present by segmenting skin tone. After performing various morphological operations on skin, it uses aspect ratio of a face to find viable face blocks. face blocks are then used to extract additional features. Therefore, one may identify and validate a person's image using their face features by using this biometric attribute. To enhance facial recognition, numerous researchers have employed Support Vector Machine (SVM), Principal Component Analysis (PCA), a combination of SVM and PCA, PCA with an Artificial Neural Network, and even the conventional PCA-SVM. Due to PCA-ANN's limited dataset limitation, PCA-SVM is superior to PCA-ANN. To extract features, relative location & size are used. If algorithm is unable to identify even one feature in a prospective face block, it rejects face block.

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