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Hesitant Fuzzy-Sets Based Decision-Making Model for Security Risk Assessment

Computers, Materials & Continua 2022年第2期70卷 2297-2317页

作者： Ahmed S.Alfakeeh Abdulmohsen Almalawi Fawaz Jaber Alsolami Yoosef B.Abushark Asif Irshad Khan Adel Aboud S.Bahaddad Alka Agrawal Rajeev Kumar Raees Ahmad Khan Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025Uttar PradeshIndia Department of Computer Applications Shri Ramswaroop Memorial UniversityLucknow-Deva RoadBarabanki225003Uttar PradeshIndia

Security is an important component in the process of developing healthcare web *** need to ensure security maintenance;therefore the analysis of healthcare web application’s security risk is of utmost *** must be considered to minimise the security ***,security risk management activities are revised,prepared,implemented,tracked,and regularly set up efficiently to design the security of healthcare web *** the security risk of a healthcare web application must be considered as the key *** is,in specific,seen as an add-on during the development process of healthcare web applications,but not as the key *** must ensure that security is taken into account right from the earlier developmental stages of the healthcare web *** this row,the authors of this study have used the hesitant fuzzy-based AHP-TOPSIS technique to estimate the risks of various healthcare web applications for improving *** approach would help to design and incorporate security features in healthcare web applications that would be able to battle threats on their own,and not depend solely on the external security of healthcare web ***,in terms of healthcare web application’s security-durability,the security risk variable is measured,and vice ***,the findings of our study will also be useful in improving the durability of several web applications in healthcare.

关键词： Web applications security risk security durability hesitantbased decision-making approach

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A Model for the Analytical Performance of Data Lake in Stock Market Analysis with Databricks Delta Lake

A Model for the Analytical Performance of Data Lake in Stock...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Kamalakkannan, S. Yasmin, A. Arunkumar, R. Kavitha, P. School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Department of Information Technology Chennai Pallavaram India Jbas College for Women Department of Computer Science Chennai India University of South Wales Faculty of Computing Engineering and Science Treforest/Newport Campus United Kingdom School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Department of Computer Applications Chennai Pallavaram India

ISBN: (纸本)9798350300857

Stock market investments are highly rewarding but also high in risk. Modern investors use variety of tools to take informed investment decisions. In the current era of digital world, financial service industry has generated huge volume and immense verities of data with extreme speed. Due to the rapid growth in data collection and the heterogeneous nature and complexity of the data, there is a need for Big Data analytical solution that would be able to deal with the stock market data. Large volumes of unstructured, heterogeneous raw data can be stored in a massively scalable manner using data lakes, which are the ideal solution to the big data storage conundrum. The ability of a data lake to preserve data in its original format while processing it at runtime using a schema on-read technique is its key feature. The challenge faced in the data lake is performing analytics which is a significant tool to calculate and analyze the stock market. The proposed architecture of Azure Databricks DeltaLake (ADDL) with Azure DataLake Storage Generation 2 (ADLSG2) is used for analytical processes like Fibonacci retracement for better stock analysis, which aid in forecasting the market price for better investment. As a result, the research focus is to produce a storage having read as well as write capabilities by taking into consideration the Extract-Load-Transform (ELT) operation on the datasource. In this experimental databricks implementation, runtime is performed using open source of Apache Spark API and a highly improved execution engine, which results in a significant performance improvement when comparing to the standard source of Apache Spark available on the ADLS platform. Additionally, the Fibonacci retracement level calculation is achieved with the analytics and forecasting of test close price with various ML and DL techniques such as KNN, LSTM are compared with original price of the test data for better prediction of forecast close price. © 2023 IEEE.

关键词： Big data

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An Integrated Approach to Energy Harvesting and Power Management Systems: A Comprehensive Analysis

An Integrated Approach to Energy Harvesting and Power Manage...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Singh, Kamred Udham Vikash Sharma, Bhavesh Kumar Singh, Teekam Varshney, Neeraj Agrawal, Dheeraj Graphic Era Hill University School of Computing Dehradun India Appventurez Mobitech Pvt Ltd Greater Noida201310 India Gl Bajaj College of Technology & Management Department of Master of Computer Applications India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Gla University Department of Computer Engineering & Applications UP Mathura India Guru Ghasidas Vishwavidyalaya Department of Information Technology Bilaspur India

ISBN: (纸本)9798350382723

Implantable devices have significantly proved their reliability and effectiveness in the field of medicine as a result of their comprehensive assistance in a variety of areas that are related to the monitoring of patients and treatment in a variety of different ways. Furthermore, they contribute to the medical community's efforts to identify and prevent illnesses via the use of their services. The fact that the devices are powered by batteries, on the other hand, is a reliance that has a number of negative consequences. For instance, its depletion might result in hindrance during surgical procedures or leakage from the body. Implicit research has proposed the use of biocompatible harvesters that are capable of sustaining the device consumption by producing power. This methodology has been proposed as a means of reducing the size of the battery or, in some circumstances, completely eliminating its utilisation. In order to develop harvesting systems, a wide variety of biocompatible materials, including as piezoelectric and triboelectric materials, may be used. Ultrasonic transducers, electromagnet generators, and photovoltaic panels are just a few examples of the low-grade power sources that may be used to power implanted medical equipment via the utilisation of bioenvironmental energy harvesting. Ultrasonic transducers have been used in a variety of research projects as energy harvesters for the purpose of providing power to implanted medical devices. A piezoelectric oscillator was implanted under the goat's skin in order to collect the vibrational energy that was created by an ultrasonic transducer that was located outside of the power that was seen to be produced by the gearbox from the model that was provided. In their study, Tower et al. presented a system that has the potential to be utilised for the purpose of biological assessment and monitoring. The energy of an ultrasonic beam that is applied to the surface is converted into a high-frequency current by this

关键词： Energy harvesting

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Performance of VM in SDN-Assisted Cloud Data Center with Working Vacations 7

Performance of VM in SDN-Assisted Cloud Data Center with Wor...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Barik, Lalbihari Dash, Bibhuti Bhusan Ranjan Mishra, Manoj Rout, Suchismita De, Utpal Chandra Shekhar Patra, Sudhansu King Abdul Aziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia KIIT Deemed to Be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350341485

The Internet of Things (IoT)'s exponentially growing network traffic has a significant impact on energy utilization and network performance. A recommended power-saving traffic control approach for cloud data centers uses virtual machines (VM) vacation. The essential idea is that the (VM) in the cloud system may go on vacation through the utilization of software-defined network (SDN) paradigm. The reduction of active VMs while upholding QoS standards is the aim for the QoS optimization. The M/M/1 queue model with working vacations (WV) and customer requests impatience behavior is examined in this research study. This study considers the possibility that the customer's impatience is caused by a WV. The requests to be served at the cloud data center by the VMs are at slow rate than normal during WVs and are prone to get impatient. An "impatience timer"that follows exponential distribution is activated whenever customer's request reached to the cloud system and discovers that the VM is on WV. A customer leaves the queue and does not come back if their service is not finished before their timer runs out. The system's numerous performance parameters such as average system size, the sojourn time, the percentage of customer's request being served, and the abandonment frequency are calculated. Finally, some numerical findings are obtained to show how some specific parameters affect the system performance indicators. © 2023 IEEE.

关键词： Energy utilization

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Integrating Artificial Intelligence and Multiple Intelligences for Advanced Educational Models

Integrating Artificial Intelligence and Multiple Intelligenc...

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2024 International Conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Palarimath, Suresh Maran, Pyingkodi Venkateswaran, R. Wilfred Blessing, N.R. Shiny, K.V. Renuga, S. University of Technology and Applied Sciences College of Computing and Information Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350378092

The aim of educational innovation is to foster students' creative and problem-solving skills via the integration of several disciplines, including science, technology, engineering, art, and mathematics. Efficiently identifying and fostering the various abilities of pupils continues to be a crucial obstacle. This research presents a sophisticated educational approach that combines the notion of multiple intelligences with artificial intelligence (AI) technology to tackle this problem. This concept improves the teaching environment by including intelligent auxiliary services for instructors and students via the use of smart speech and picture interaction. Artificial intelligence (AI) integrated into the multiple intelligence's framework enables the ability to observe, analyze, and implement personalized teaching tactics in real-time using machine learning. The suggested AI-assisted education paradigm aims to enhance teacher-student interactions and facilitate personalized instruction. The study provides evidence that this technique successfully facilitates personalized and captivating learning experiences, promoting students' diverse talents and augmenting their creativity and problem-solving capabilities. This approach seeks to fundamentally transform conventional educational techniques by addressing the disparity between existing educational practices and the future requirements of the workforce. © 2024 IEEE.

关键词： Adversarial machine learning

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Increasing Productivity in Software Development Through the Use of Docker technology 1

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3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Sheikh, Anisha School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering and Applications GLA University UP Mathura India Department of Information Technology Guru Ghashi Das University Bilaspur Bilaspur India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

The Docker technology makes the process of developing software substantially more effective by resolving issues related to deployment and hosting. Through the use of virtualization, it is able to function independently of the underlying hardware infrastructure, and it relies on its own code to carry out the execution of programs. The encapsulation of an application's whole environment together with its source code is at the heart of Docker's methodology. This enables Docker to efficiently eliminate the problems that are associated with having a variety of working environments, but it may also result in the images having a greater file size. "Images" is the term that Docker uses to refer to these bundled environments. Docker was used in this investigation to carry out a variety of tests, which allowed identical code to be executed on a variety of platforms, so permitting extensive comparisons. Software developers made use of Docker's ability to analyze the outcomes, which enabled them to make decisions based on accurate information. This study simplifies the software development and deployment procedures for the development community by providing significant insights into the benefits and considerations of Docker across numerous platforms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Python

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Soil Analysis Using Deep Learning for Precision Agriculture 1

Soil Analysis Using Deep Learning for Precision Agriculture

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1st International Conference on Pioneering Developments in Computer Science and Digital Technologies, IC2SDT 2024

作者： Fotabong, Thierry Forsack Muhammad, Abdullahi Keisham, Murphy Mehrotra, Tushar Singh, Rajneesh Kumar Singh, S. Pratap Agrawal, Arun Prakash Sharda School of Engineering and Technology Sharda University Department of Computer Science and Applications Greater Noida India School of Computing Science and Engineering Galgotias University Department of Computer Science and Engineering Greater Noida India Symbiosis Institute of Technology Nagpur Campus Deemed University Pune India

ISBN: (纸本)9798350365016

This research explores the application of deep learning techniques for soil analysis in precision agriculture, focusing on the classification of soil types using convolutional neural networks (CNNs). The study addresses the limitations of traditional soil classification methods, which are often labor-intensive and reliant on expert judgment. The proposed approach involves collecting soil image data, preprocessing it using techniques such as Gabor filtering, and classifying the images with a CNN model. The methodology includes data acquisition, feature extraction, and model training, aiming to accurately predict soil parameters using diverse input data sources like sensor readings and satellite imagery. The potential benefits of this approach include real-time monitoring, optimal fertilizer application, and enhanced crop management, which can contribute to sustainable agricultural practices and food security. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring Sensor-Based Smart Farming Technologies in the Internet of Things (IoT) 1

Exploring Sensor-Based Smart Farming Technologies in the Int...

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1st IEEE International Conference on computing and Data Science, ICCDS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Balakumar, C. Sujatha, T. Wilfred Blessing, N.R. College of Computing and Information Sciences University of Technology and Applied Sciences Information Technology Department Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350365337

The Internet of Things (loT) is a new technology trend that is being used in almost every area of human life. IoT is used almost every aspect of people's lives. Significantly, with a projected increase in the world's population to 9.7 billion by 2050, agricultural output would need to increase at an even more rapid rate to fulfill the requirement. Modern tools, notably the Internet of Things, make this a reality. The IoT makes it possible for farms to function without human labor. It has several potential applications in agriculture, including large- scale farming, greenhouse farming and management. The sensors serve as the most crucial component of the loT. Sensing devices are mostly used for the purpose of learning about the soil and its surroundings. The sensor has several applications in agriculture, including but not limited to NPK (nitrogen, phosphorus, and potassium) measurement, disease detection, and soil moisture analysis. This study discusses how IoT applications contribute to efficient farming practices. It shows how the IoT can be applied to agriculture and exhibits the many sensors, applications, problems, strengths, and shortcomings that underpin this field. © 2024 IEEE.

关键词： Internet of things

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Asthma Disease Risk Prediction Using Machine Learning Techniques

Asthma Disease Risk Prediction Using Machine Learning Techni...

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2023 International Conference on Computer Communication and Informatics, ICCCI 2023

作者： Pyingkodi, M. Thenmozhi, K. Wilfred Blessing, N.R. Selvaraj, P.A. Kumar, Kamdev Aadarsh, V. Mariya Arockiya Akash, A. Kongu Engineering College Department of Computer Applications Erode India Kristu Jayanti College Department of Computer Science Bengaluru India University of Technology and Applied Sciences-Ibri College of Computing and Information Sciences IT Department Oman

ISBN: (纸本)9798350348217

The signs of asthma, a long-term inflammatory condition of the airways, include wheezing, throat tightness, coughing, and breathing difficulties. The attack of an asthma, which can be fatal, is the fast worsening of these symptoms. Another severe, irreversible airflow restriction in the lungs is caused by respiratory COPD, which encompasses emphysema and chronic bronchitis. In this project, a machine learning-based algorithm is for predicting asthma risk is presented (ML). PEFR, which are widely used external tools like peak flow meters and recognized asthma risk predictors, are frequently monitored. This study shows a relationship between the ambient particle matter(PM) and the weather outdoors. According to the best peak flow value each person was able to acquire, the results are divided into two groups: Safe and Risk circumstances. The link between indoor PM and weather data is mapped to the found values using a convolutional neural network (CNN) architecture. The suggested method's root mean square and mean absolute error accuracy metrics are contrasted with those of current deep neural network (DNN)-based methods. Additionally, the accuracy of the classification methods KNN and SVM are carried out. The new data set's asthma category may be predicted more accurately thanks to the application of SVM, KNN, and CNN classification. Python 3.7 is the coding language employed. © 2023 IEEE.

关键词： Support vector machines

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Differential and TriPhase Adaptive Learning-Based Privacy-Preserving Model for Medical Data in Cloud Environment

IEEE Networking Letters

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IEEE Networking Letters 2022年第4期4卷 217-221页

作者： Gupta, Rishabh Saxena, Deepika Gupta, Ishu Singh, Ashutosh Kumar National Institute of Technology Kurukshetra Department of Computer Applications Kurukshetra136119 India National Sun Yat-sen University Cloud Computing Research Center Department of Computer Science and Engineering Kaohsiung804 Taiwan

This letter proposes a novel secure data protection model for privacy preservation in the cloud environment by partitioning, sanitizing, and analyzing the data effectively to improve the model's privacy. Several experiments and comparisons of the proposed model with existing works indicate that it protects data with high accuracy, precision, recall, and F1-score up to 87.03%, 84.87%, 87.03%, and 85.00%, for diverse datasets with a relative improvement up to 15.89%, 27.73%, 15.89%, and 21.44%, respectively. © 2019 IEEE.

关键词： Deep neural networks

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