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

作者： Garg, Kamal Garg, Srishti Dixit, Himanshu Pandey, Kavita Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Noida2311 India

ISBN: (纸本)9798350379945

Mental stress has been a major issue among all age groups today. This is more like a stigma in the society where people don't even want to talk about it openly leading to severe health issues. Nowadays, people are deeply engaged with social media platforms for connection and self-expression of their emotions. They express all these through posts, photos, and comments. Thus, social media platforms can help in predicting the mental state and well-being of an individual. Prediction of a person's mental health can help to undertake some preventive measures. Various researchers have worked around this topic but there is no evidence of utilizing the most active web of people for obtaining the most accurate results. Machine Learning (ML) algorithms are used in this research study to identify whether a person is mentally stressed or not based on the social media data obtained from the user's post or by using a text input of around hundred words from users related to their day-to-day activities. This research study has used a decision tree, random forest, and Bidirectional Encoder Representations from Transformers (referred as BERT onwards) model classifier to train models. This study has used a dataset of Reddit (a social media platform) posts text data for training the models. The primary objective is to compare the efficacy of various machine learning models in predicting mental health based on social media data. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimal design of single-tuned filters for industrial power systems to reduce harmonic distortion using forest algorithm

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Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A 2024年第4期47卷 414-422页

作者： Lin, Shu-Min Chou, Chih-Ju Lu, Chia-Te Hsiao, Ying-Tung Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Computer Science National Taipei University of Education Taipei Taiwan

This paper presents a novel optimization algorithm for designing single-tuned filters in industrial power systems to reduce harmonic distortion. Single-tuned filters are cost-efficient, simple, and easy to maintain. We formulate the filter design as an optimization problem, minimizing costs while incorporating harmonic limits and system constraints. Our solution algorithm simulates tree growth, proliferation, and death based on the forest algorithm to generate optimal solutions. This enables both local and global searches for fast convergence and feasible designs. Unlike traditional approaches, we account for variations in filter components and system parameters caused by factors like manufacturing tolerances, operating temperature, and frequency variations. Our study contributes by designing adaptive filters that match practical systems. We validate our method against previous publications and other techniques like simulated annealing and teaching-learning-based optimization. Simulation results demonstrate the effectiveness of our approach in minimizing costs, satisfying operation constraints, and accounting for component variations in this complex problem. © 2024 The Chinese Institute of Engineers.

关键词： Power quality

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Analysis of Bigmart Outlets sales using Big Data Analytics Method 2

Analysis of Bigmart Outlets sales using Big Data Analytics M...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Ramasami, Meaga Varsha Thangaraj, Rajasekaran Kumar, S Manoj Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Centre for IoT and Ai Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665456302

Sales prediction is an important problem for businesses. A sales forecast can guide a company's production, pricing, and promotion strategies, and can help mitigate risks associated with under or overproduction. Traditional methods of sales prediction utilized statistical techniques like regression analysis or time series analysis, but these methods have certain limitations, including the need for large and clean data sets. This research work focuses on exploring the patterns and trends present in a large-scale retail dataset through exploratory data analysis (EDA). The primary objective of this research is to uncover insights that can enhance the understanding of customer behaviour, identify opportunities to improve business processes and inform data-driven decisions. The data consists of daily sales records for 1560 products of ten BigMart Outlets. This study explores several preprocessing steps, such as handling missing values and outliers, scaling and normalization, and feature engineering. The findings also revealed that the top-selling items were bought by a diverse range of customer demographics, indicating that marketing campaigns should target a broader audience. Furthermore, the study identified several unusual transactions that required further investigation. © 2023 IEEE.

关键词： Sales

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Securing the Data using Steganography Technique in Images 14

Securing the Data using Steganography Technique in Images

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Sharath, M.N. Anil Kumar, K.N. Arjun, B.C. Harshitha, S. Department of Computer Science and Engineering Rajeev Institute of Technology Hassan India Department of Information Science and Engineering Rajeev Institute of Technology Hassan India Department of Computer Science and Engineering Malnad College of Engineering Hassan India

Since ancient times, it has been necessary to conceal secret communications and tranfer information without leaks. In today’s technology, all available digital media, such as photographs, sound files, films, and so on, provide a rich environment for hiding information that the human eye cannot detect and exploiting unused or redundant data bits. This information can be used to convey messages to hidden agents, but it can also be used as a pressure technique to deliver additional messages within a single file, minimizing the amount of data that must be transferred. This paper demonstrates two methods for encoding/storing a message within the RGB pixels of a cover image (canvas image) without causing visual distortion. A message is broken up into several chunks, converted to 8-bit binary values, encrypted with a simple symmetric exclusive OR (XOR) encryption key, and then encoded into a canvas image by changing the least significant bit (LSB) of the pixel value in both directions. The first approach saves the secret message sequentially, beginning with the top-left pixel, and then encodes the secret message from top to bottom and left to right. The second method randomly encodes the cover image's pixels with the hidden image's message bits, making the concealment less sensitive to scrutiny. © Grenze Scientific Society, 2023.

关键词： Steganography

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Performance Analysis of Disruptive Instances in Cloud Environment 16

Performance Analysis of Disruptive Instances in Cloud Enviro...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Nandy, Pranab Saha, Rounak Satpathy, Anurag Chakraborty, Sandip Addya, Sourav Kanti National Institute of Technology Karnataka Cloud and Smart System Services Lab Department of Computer Science and Engineering Surathkal India Missouri University of Science and Technology Cloud and Smart System Services Lab Department of Computer Science and Engineering United States Indian Institute of Technology Kharagpur Cloud and Smart System Services Lab Department of Computer Science and Engineering India

ISBN: (纸本)9798350383119

Virtualization enables the service providers (SPs) to logically partition the resources into virtual machines (VM) instances. Real-world SPs such as Amazon, Google, Microsoft Azure, IBM, and Oracle provide different flavors of VM instances, such as on-demand, reserved, and low-cost or spot, depending on the type of application hosted. The on-demand instances are short-term and typically incur a higher cost than reserved instances that are provisioned for a longer duration at a discounted rate. Low-cost or spot instances are cost-effective compared to on-demand but are reclaimable by the SPs. The SPs often claim that the on-demand and spot instances achieve similar performance, but it is far from that. This paper studies the performance of spot instances via rigorous experimentation over commercial SPs such as Amazon AWS and Microsoft Azure. Real-world evaluations affirm that spot instances perform poorly compared to their on-demand counterpart concerning memory, CPU, disk read, and write operations. We identify such instances as disruptive and name them so because it does not fulfill the performance, durability, and flexibility expectations like an on-demand instance having the same configuration. We also perform hypothesis testing over the experimental data obtained to corroborate our claim further. © 2024 IEEE.

关键词： Cloud Computing Disruptive Instance On-demand Spot Instance

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Remote Sensing-Based Earth Climate Detection in Geoscience Model with Artificial Intelligence Application

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 569-581页

作者： Agarkar, Aarti Amod Karyakarte, Mandar S. Chavhan, Gajanan Ariffin, I.A. Patil, Milind Ushasree, Linginedi Priya, D. Divya Vishwakarma Institute of Technology Pune India Management and Science University Selangor Shah Alam Malaysia Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Remote sensing technology can offer exact information in an image format, it has permeated every sector related to natural resources. Right now, the source of geographic area data that is expanding the fastest is satellites used for remote sensing. Change detection is a highly valuable tool for monitoring environmental and human demands, given the constant changes in the earth’s surface and the extensive use of remote sensing. A significant capacity for estimating climate change has been made possible by combining machine learning (ML) models with remote sensing imagery. This methodology takes less data than traditional methods and relaxes a number of assumptions. Influential variables for estimating climate change using machine learning models are provided by satellite imagery. In this research, climate change detection has been analysed for geoscience modelling based on remote sensing data and ML models. Remote-sensed region images have been collected in which the pre-processing is carried out for noise removal, normalisation, and smoothening. This processed image’s features have been selected using a linear principal recurrent component neural network and classified using a random graph SqueezeNet perceptron neural network. The simulation results have been analysed for various remote-sensed region image data in terms of random accuracy, average precision, RMSE, recall, and F-measure. The proposed technique has a random accuracy of 96%, average precision of 97%, F-measure of 87%, RECALL of 85%, and RMSE of 61%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate change detection Geoscience modelling Random graph perceptron Recurrent component Remote sensing data

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Data Visualization for the Dataset Collected from Education Sector using Python 1

Data Visualization for the Dataset Collected from Education ...

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1st IEEE International Conference on Communications and computer science, InCCCS 2024

作者： Dol, Sunita M. Jawandhiya, P.M. Computer Science and Engineering Department Walchand Institute of Technology Maharashtra Solapur413006 India Computer Science and Engineering Department Pankaj Laddhad Institute of Technology and Management Studies Yelgaon Maharashtra Buldana443002 India

ISBN: (纸本)9798350358858

Visualization is the representation of information in the form of various charts or images. Data visualization is used to identify useful patterns, to understand trends, and to find out outliers in dataset. Data visualization is applied in all sectors such as medical, engineering, etc. for analyzing the data. Data visualization is also useful to instructor as well as students in education sector. In this research article, we have used the Python Programming Language features to visualize the data collected from university related to Four Years computer science and engineering Programme related to result analysis ledgers of students for the Academic Year 2014-15 and 2015-16. Python features such as streamlit, pandas, altair, and random are considered to create the visualization of data. Various charts such as Pie chart, Scatter plot, Box plot, Density chart, Heatmap chart, Distribution Box Plot, Distribution Histogram, Distribution Line, and Distribution Swarm are used to visualize the data Teacher point of view, Students point of view, and Course-wise. From teacher/ instructors point of view, it is used to analyze the data semester-wise, and Yearwise for grade distribution, marks distribution, and percentile distribution while from students/ learner’s point of view, data visualization examine the data semester-wise, and Year-wise for marks and grade analysis of particular student. In case of course-wise part of visualization, we can see students-wise grade and marks of particular course, course-wise distribution, course-wise Distribution Histogram, course-wise Distribution Line, and course-wise Distribution Swarm. © 2024 IEEE.

关键词： Data visualization

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Machine Learning-Based Election Results Prediction Using Twitter Activity

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SN computer science 2024年第7期5卷 819页

作者： Kumari, Shweta Singh, Maheshwari Prasad Department of Computer Science and Engineering National Institute of Technology Patna Bihar Patna India Department of Information Technology Muzaffarpur Institute of Technology Bihar Muzaffarpur India

Elections are a vital component of democracy because they allow citizens to exercise their right to vote and choose representatives at all levels of government. The exponential growth of social media platforms has facilitated more accessible communication between political parties, politicians, and voters, disseminating distinctive political messages. Twitter, one of the most liked social media platforms, has become a valuable source for real-time public opinion. The proposed work uses sentiment analysis to predict electoral outcomes. Through analyzing the sentiment expressed in tweets regarding political candidates or parties, the machine learning algorithm, Long Short-Term Memory (LSTM), can be trained to identify patterns and correlations between sentiment and election results. The work examines the efficacy of the suggested approach by analyzing two large datasets of tweets during a specific election period. The proposed work presents a hybrid multimodal framework that incorporates emojis and multilingual text, including Hindi, English, and Hinglish (tweets containing both Hindi and English). The model is validated on datasets containing these multilingual elements and additionally tested on English-language tweets to evaluate its performance across different linguistic contexts. The results demonstrate the predictive capability of sentiment analysis on Twitter activity, shedding light on its potential as a supplementary tool for election forecasting. The outcome of the proposed method overviews Rahul Gandhi and Narendra Modi's polarity and subjectivity values. The accuracy of the proposed work is 87.65%, which outperforms the Twitter dataset used in state-of-art work. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Election Machine learning Polarity Sentiment analysis Twitter

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Adversarial Attacks Against Double RIS-Assisted MIMO Systems-Based Autoencoder in Finite-Scattering Environments

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IEEE Wireless Communications Letters 2025年第6期14卷 1593-1597页

作者： Son, Bui Duc Khanh, Ngo Nam Chien, Trinh Van Kim, Dong In Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of Hanoi University of Science and Technology School of Information and Communication Technology Hanoi100000 Viet Nam

Autoencoder permits the end-to-end optimization and design of wireless communication systems to be more beneficial than traditional signal processing. However, this emerging learning-based framework has weaknesses, especially sensitivity to physical attacks. This letter explores adversarial attacks against a double reconfigurable intelligent surface (RIS)-assisted multiple-input and multiple-output (MIMO)-based autoencoder, where an adversary employs encoded and decoded datasets to create adversarial perturbation and fool the system. Because of the complex and dynamic data structures, adversarial attacks are not unique, each having its own benefits. We, therefore, propose three algorithms generating adversarial examples and perturbations to attack the RIS-MIMO-based autoencoder, exploiting the gradient descent and allowing for flexibility via varying the input dimensions. Numerical results show that the proposed adversarial attack-based algorithm significantly degrades the system performance regarding the symbol error rate compared to the jamming attacks. © 2012 IEEE.

关键词： Decoding Vectors Reconfigurable intelligent surfaces Finite element analysis Fading channels Correlation MIMO communication Jamming Optimization Channel state information

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Urban Electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier

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computers, Materials & Continua 2024年第7期80卷 1163-1177页

作者： Amel Ali Alhussan Doaa Sami Khafaga El-Sayed M.El-kenawy Marwa M.Eid Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute for Engineering and TechnologyMansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric *** demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of *** establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this ***,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of *** the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging *** is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic *** vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering ***,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature ***,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic *** on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is *** results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algori

关键词： Electric vehicle graylag goose optimization metaheuristics optimization machine learning

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