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2024 International Conference on Smart Devices, ICSD 2024

作者： Dumka, Ankur Pandey, Arnav Mishra, Amit Kumar Bhaskarrao Bahadure, Nilesh Tripathi, Neha Pandey, Neeraj Kumar Women Institute of Technology Dept. of Computer Science & Engineering Dehradun India St. Joseph's Academy Dehradun India Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India Symbiosis Institute of Technology Nagpur Campus Pune India Dept. of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350394412

In the economy where trade and finance are closely linked it is essential to accurately identify and authenticate various currencies, especially in industries such, as banking, tourism and retail. Conventional methods, for identifying coins typically require inspections or simple automated processes, which might take up a lot of time and are susceptible, to mistakes. The advancements, in machine learning (ML) and deep learning (DL) technologies, over the years have revolutionized currency detection providing efficient solutions. This article discusses the evaluation of machine learning and deep learning approaches to identify types of coins. It explores how algorithms, like MobileNetV2, Support Vector Machines (SVM), Random Forest, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) perform in distinguishing and classifying coin denominations across currencies. The results of experiments conducted on data sets show how these methods compare in terms of accuracy, effectiveness, and reliability. Furthermore, we examine the advantages and constraints of each approach while suggesting directions for advancing research, in the realm of identifying cryptocurrency. © 2024 IEEE.

关键词： Convolutional neural networks

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Remote Sensing–Based UAV Imaging in Heat Pattern Analysis Impact on Climate Change Detection Using Fuzzy Stacked Lasso Elastic-Net Model

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 699-708页

作者： Sailaja, M. Kumar, M. Prema Jyothi, B. Swarna Vanguri, G. L. Narasamba Manjula, S. Priya, D. Divya Department of Computer Science and Engineering Ravindra College of Engineering for Women Andhra Pradesh Kurnool India Dept. of ECE Shri Vishnu Engineering College for Women (A) Andhra Pradesh Bhimavaram 534202 India Department of Computer Science & amp Engineering Madanapalle Institute of Technology & amp Science Andhra Pradesh Madanapalle India Information Technology Aditya College of Engineering & amp Technology(A) Andhra Pradesh Surampalem 533437 India Department of Computer Science and Business Systems Nehru Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Urban heat islands raise surface temperatures, which has an effect on city dwellers’ health and welfare. Urbanisation-related changes to the land surface, which are especially notable right after sunset, have an impact on radiative forcing. Recently, there has been a surge in the usage of unmanned aerial vehicle (UAV) technology, particularly for precision agriculture and plant phenotyping. This is due to the declining cost and increased accessibility of both UAVs and thermal imaging sensors. This interruption affects subsequent operations like target item categorisation and water quality parameter inversion. The goal of this study was to evaluate the potential for combining machine learning (ML) with unmanned aerial vehicle (UAV)–based imaging technology. In this research, the novel technique in UAV image–based temperature pattern detection of various regions of earth and their sea level rise analysis using image classification by machine learning model. Here, the input is collected as UAV images which have been processed for noise removal and normalisation. These image features have been extracted using convolutional active contour–based kernel component analysis and classified using region-based fuzzy stacked lasso elastic-net model. The classified output shows temperature analysis with sea level rise based on image analysis. The experimental analysis has been carried out for various UAV image dataset in terms of detection accuracy, RMSE, F-measure, mean average precision (MAP), and recall. Proposed technique F-measure of 89%, detection accuracy of 96%, MAP of 94%, recall of 92%, and RMSE of 60%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional active contour Heat pattern detection Kernel component analysis Lasso elastic-net model Remote sensing

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Spam Detection Using Firefly Optimization Technique 2

Spam Detection Using Firefly Optimization Technique

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Srivastav, Shashank Gupta, Alok Singh, Laxman Singh, Rajesh Kumar Tripathi, Kanak Dwivedi, Anshu Kumar Buddha Institute of Technology Department of Computer Science Engineering Gorakhpur India Babu Banarasi das Engineering College Department of Computer Science Engineering Lucknow India Bennett University School of Computer Science Engineering and Technology Greater Noida India KIPM College of Engineering & Technology Dept of CSE Gorakhpur India Galgotia College of Engineering & Technology Dept of MCA Greater Noida India

ISBN: (纸本)9798350368109

In this electronically era, communication using email has become an part of our lives. However, with the convenience of email comes the nuisance of spam messages flooding our inboxes. Detecting and filtering out these spam emails is crucial to maintain productivity and security in our online interactions. In this research project, we explore the performance of using the firefly algorithm, a nature-inspired optimization technique, for spam detection. By leveraging the unique behavior of fireflies in search and optimization, we aim to develop a robust and efficient spam detection system. We start by gathering a dataset containing a mix of spam and non-spam (ham) emails. After preprocessing the text data and extracting relevant features using techniques like Count-Vectorizer, we train a logistic regression model, a commonly used classifier for binary classification tasks. To enhance the performance of our model, we employ the Firefly algorithm to optimize its hyperactive parameters. This algorithm impersonates the flashing behavior of fireflies to iteratively search for the foremost combination of model parameters, maximizing its ability to distinguish between spam and legitimate emails. Our experimental results demonstrate promising outcomes, with the optimized model achieving high accuracy and performance measures such as precision, recall, and F1-score on a held-out test set. These findings highlight the potential of nature-inspired algorithms like the firefly algorithm to improve spam detection techniques and effectively combat email spam in real-world scenarios. © 2024 IEEE.

关键词： Logistic regression

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An Intelligent Security Systems in Cloud Computing using Cryptography Techniques 5

An Intelligent Security Systems in Cloud Computing using Cry...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Srilakshmi, V. Chetana, V. Lakshmi Dornala, Raghunadha Reddi Vallabaneni, S. Parvathi Ramanjaiah, Ganji School of Computer Science and Engineering Vit -AP University Andhra Pradesh Amaravati522237 India School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amaravati Campus India Walgreens United States Pvp Siddhartha Institute of Technology Department of It Vijayawada India R v Institute of Technology Dept.of Cse Guntur India

ISBN: (纸本)9798350379945

Delivering computing resources over the internet has seen a paradigm shift towards cloud computing. However, there are serious security risks because cloud environments are distributed and shared. Intelligent Security Systems (ISS) that use cryptographic techniques are now necessary to overcome these issues, such as data confidentiality, integrity, and authenticity;this study developed an ISS for cloud computing, concentrating on cryptographic techniques. The RSA-OAEP (Optimal Asymmetric Encryption Padding) and X.509 algorithms are combined to create the ISS and further cover a variety of cryptographic methods, emphasizing their functions in protecting cloud-based systems, including encryption, digital signatures, and key management. It also looks at improving security systems' threat detection and response capabilities by integrating artificial intelligence (AI) algorithms like Grubbs' Test. In addition, several issues and potential paths for intelligent security systems in cloud computing are examined, highlighting the necessity of ongoing study and development to remain ahead of developing cyber threats. © 2024 IEEE.

关键词： Cloud security

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A comparative study of different scheduling approaches for splittable latency sensitive tasks in Fog-Cloud environment 2

A comparative study of different scheduling approaches for s...

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2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Sandeep, Kone Sai Koundinya, Challa Aditya Prabhas, Adapa Venkata Swain, Chinmaya Kumar Mishra, Sambit Kumar SRM University Dept. of Computer Science Engineering India

ISBN: (纸本)9798350344882

IoT has revolutionized the way we live and the work we do by connecting different devices through the Internet. In the present scenario, the number of IoT devices are increasing rapidly due to the increase in technology and the increase in the comforts of life. Nowadays we can see that many of them are using IoT devices regularly, it's estimated that by the end of 2030, there will be 30 billion users who will be using IoT applications. These devices send data to the cloud for processing. Due to the distance of the cloud from the IoT devices, the application requests get delayed service responses. So to handle the latency sensitive applications we require the micro cloud service like fog servers deployed near to the data generation points. The fog layer lies between the IoT devices and Cloud which acts as an intermediate layer. This helps in reducing latency of the tasks and provide better performance. As the number of IoT applications keeps on increasing, the resources available with the fog nodes may not handle the upcoming demands. So to overcome these demands, we are using splittable methods to allocate the tasks to Fog/ Cloud nodes more compactly. If a task can be splitted before the deadline into different modules, then we split the given task and allocate those tasks to different fog nodes/ servers and then collecting back the data from the fog nodes/ servers and merging them into a single unit. With the help of this method, we can increase the performance of the system. © 2023 IEEE.

关键词： Fog computing

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Network Intrusion Detection System Using Decision Tree and KNN Algorithm 3

Network Intrusion Detection System Using Decision Tree and K...

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3rd International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2024

作者： Singh, Km Shivani Singh, Khundrapam Johnson National Institute of Technology Dept. of Computer Science and Engineering Manipur Imphal India

ISBN: (纸本)9798350309409

The complexity of attacks is increasing, making it increasingly difficult to effectively discover breaches. A network intrusion detection system is needed to handle the aforementioned problem. An automated software program called a network intrusion detection system alerts administrators when someone attempts to compromise the system by participating in hazardous behaviors. A system or network is protected from harmful invasions by hardware and software firewalls. They function like filters, removing any data that could put the system or network at risk. We aimed to design the best intrusion detection system possible so that it could distinguish between 'normal' and 'attacked' categories of network data with high accuracy. The best accuracy can be obtained by using soft computing techniques like Decision Trees and KNN. We tested a variety of techniques, such as preprocessing the data, feature selection, principal component analysis (PCA) reduction, standardization, and normalization, in order to increase our model's accuracy scores. We also assess the two approaches' results according to their accuracy in order to decide which way is more effective. © 2024 IEEE.

关键词： Decision trees

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A Machine Learning Approach to Find Torn Ratio of Bangladeshi Currency 5

A Machine Learning Approach to Find Torn Ratio of Bangladesh...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Hossen, Md. Delwar Opi, Kazi Sabbir Ahmed Ahmmed, Tushar Mahmud, Tamim Al Green University of Bangladesh Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350394290

Paper currency can be torn by numerous factors, including careless use, over-circulation, exposure to harsh environments, and so on. Torn money can cause financial losses, disrupt commerce, and demented people whether they accept it or not. The state-of-the-art system is a semi-automated system that calculates torn ratio through dimension. This paper presents the estimation of the torn ratio of Bangladeshi currency. To mitigate this torn money problem, we have developed a method to solve this issue. The proposed solution uses the SIFT (Scale-Invariant Feature Transform) to image feature extraction with the K-nearest neighbor algorithm for image perspective in the preprocessing part. For the elimination of outliers and to get accurate estimation, a Homography matrix is used. After that, the torn ratio was calculated according to its RGB color value. The OpenCV library is used to design and implement the proposed algorithm. The algorithms achieved impressive accuracy highlighting the robust performance of the implemented model. © 2023 IEEE.

关键词： Nearest neighbor search

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Airline Sentiments Unplugged: Leveraging Deep Learning for Customer Insights 5

Airline Sentiments Unplugged: Leveraging Deep Learning for C...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Arpita, Habiba Dewan Al Ryan, Abdullah Hossen, Md Shakil Rahman, Md. Sadekur Daffodil International University Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350394290

Over the past twenty years, the competitive airline industry has expanded at an exponential rate. As more people travel through various airlines, they encounter more amenities and issues, and based on those encounters, they offer their personal reviews. This study significantly advances the area of sentiment analysis in the airline industry by giving businesses the means of extracting insightful information from customer reviews and use that information to inform data-driven service improvement choices. The review of airline programs effectiveness heavily relies on feedback from consumers. An extensively gathered dataset of 67,993 reviews from the Google Play Store and the App Store based on the ten most well-known airlines, was divided into three categories: 'Positive,' 'Negative,' and 'Neutral'. Word embedding technique was integrated with deep learning models, such as CNN, LSTM, and BiLSTM to improve sentiment analysis accuracy. Both the LSTM and BiLSTM models achieved remarkable accuracy rates at 90% and 91% respectively, which is intriguing. However, BiLSTM was found to outperform other deep learning models regarding precision at 92%, recall at 91%, and F1-score at 91% among other outcomes. © 2023 IEEE.

关键词： Classification (of information)

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Predicting Mobile Prices with Machine Learning Techniques

Predicting Mobile Prices with Machine Learning Techniques

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

作者： Rana, Deepak Singh Dhondiyal, Shiv Ashish Singh, Sumeshwar Kukreti, Sanjeev Dhyani, Ashish Graphic Era Hill University Dept. Of Computer Science And Engineering Dehradun India Graphic Era Deemed To Be University Dept. Of Computer Science And Engineering Dehradun India Jaypee Institute Of Information Technology Dept. Of Computer Science And Engineering India Graphic Era Deemed To Be University Department Of Hospitality Management Uttrakahnd Dehradun India

ISBN: (纸本)9798350306446

Smartphones are increasingly vital to people on a daily basis. Telephones are utilized in all aspects of life, ranging from personal to professional, due to technological advancements. It serves a function beyond making phone calls. It enables internet connectivity and email reading. when not using the computer. The characteristics of a mobile phone are a crucial consideration when buying *** overall objective of this research is to find the best way to apply machine learning to estimate the retail pricing of smartphones based on their individual specs. Individuals who frequently use their phone are more attentive to selecting features. When purchasing a cell phone, a comparison is done based on the price-performance ratio. Phone features are regarded as performance. This research aims to forecast if mobile phones with certain features are considered economical or *** work is capable of being utilized in various marketing and business contexts to assist in making informed purchasing decisions by maximizing features while minimizing costs. © 2024 IEEE.

关键词： Decision trees

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Smart Mushroom Cultivation: Enhancing Growth Conditions through Sensor-Based Monitoring

Smart Mushroom Cultivation: Enhancing Growth Conditions thro...

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Rana, Ishaan Gupta, Ankit Mittal, Rakshit Chaudhary, Harshita Tyagi, Shobha Chawla, Pronika Mriirs Dept. of Computer Science and Engineering Faridabad India

ISBN: (纸本)9798350344387

Smart mushroom cultivation using remote sensing involves the integration of advanced technology to optimize the growth and productivity of mushrooms. Remote sensing techniques using arduino, sensors and cloud services are employed to monitor and manage various farming and growth related parameters in mushroom farms. Using the remote sensing of mushroom fields provides a macroscopic view of environmental conditions, helping farmers identify suitable locations for mushroom cultivation based on factors like temperature, humidity, and vegetation cover. Ground-based sensors are strategically placed within the cultivation area to measure crucial variables like temperature and humidity levels. These sensors transmit data to a central control system, allowing farmers to maintain optimal conditions for mushroom growth. Automated climate control systems can adjust parameters in response to real-time data, ensuring consistent and ideal growing conditions. This enables timely intervention, reducing crop loss and the need for chemical treatments. Furthermore, remote sensing can improve resource management. Precise water and nutrient monitoring based on sensor data can lead to more efficient resource utilization, reducing waste and operating costs. In summary, smart mushroom cultivation using remote sensing technology optimizes environmental monitoring, and resource usage. By harnessing the power of remote sensing, mushroom farmers can increase yields, reduce environmental impact, and enhance the overall sustainability and profitability of their operations. © 2023 IEEE.

关键词： Fans Fuzzy logic Humidity Schedules Temperature

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