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检索条件"任意字段=8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023"

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Purchases Insights using Alteryx as Self-Service Analytics 8

Purchases Insights using Alteryx as Self-Service Analytics

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8th international conference on Communication and Electronics systems, ICCES 2023

作者： Samuel, Jackson Maheswaran, C.P. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350396638

the ability to transform data into usable information is the basis of the success of any business organization. Traditional data analytics processes and tools are generally slow, can't keep up with the changing times, are painfully tedious to maintain and process, and don't meet the analytical standards organizations require. In a business where time is of the essence for every operation and decision, any delay in identifying opportunities to scale or making business decisions that will impact the organization in the long term would prove to be *** study demonstrates how effectively Alteryx enables a company to leverage behavioral data from customer buying trends to create and maintain dashboards that provide an at-a-glance view of all key senior executives' key performance indicators (KPIs). the developed Alteryx Analytic App is a self-service analytics tool that allows senior management to mine data and obtain results-based business insights, to increase employee efficiency by increasing productivity without any intervention from other developers and employees. © 2023 ieee.

关键词： Machine learning

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Integrated Deep Learning and Machine Learning-based Approach for Automatic Real-Time Skin Lesion Prevention and Diagnosis Model 8

Integrated Deep Learning and Machine Learning-based Approach...

引用

8th international conference on Communication and Electronics systems, ICCES 2023

作者： Bhutnal, VinodKumar Rathod, Lakhan Naik, Janvi Jagadale, Sonali JSPM's Rajarshi Shahu College of Engineering Department of Computer Engineering Tathawade Maharashtra Pune411033 India

ISBN: (纸本)9798350396638

the skin is a remarkable organ that acts as a defense against viruses, ultraviolet radiation, and other environmental dangers. While playing a vital function, the skin is susceptible to a variety of illnesses and ailments, many of which can have a serious negative effect on a person's quality of life. It occasionally struggles with a range of ailments. As a result, treating or simply identifying human skin diseases can be challenging. It is vitally important to identify them because the majority of skin diseases or skin disorders are caused by a lack of medical infrastructure and/or knowledge. To categorize skin disorders using image processing, the study employs real-time or image-picture data. A deep learning technique might be used to achieve this. Deep learning methods like VGG-19 and DenseNet can be used to achieve these. this study will help to identify the disease in its early stages in order to prevent it to a certain degree by visualizing the disease's symptoms, then provide the most effective preventative measures and diagnosis-based remedies. © 2023 ieee.

关键词： Convolutional neural networks

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Text Classification Into Emotional States using Deep Learning based BERT Technique 8

Text Classification Into Emotional States using Deep Learnin...

引用

8th international conference on Communication and Electronics systems, ICCES 2023

作者： Priyanka, Madineni Karthik, Pushadapu Yalla, Prasanth Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur AP Vaddeswaram India

ISBN: (纸本)9798350396638

Sentiment analysis, a subset of Natural Language Processing (NLP), has grown in relevance as the modern lifestyles have become more reliant on social media. Emotional expressions include actions, feelings, ideas, and nonverbal cues. Modern approaches for Natural Language Processing (NLP) have their origins in machine learning, notably statistical machine learning. this study introduces BERT, an innovative technique for reading sentiments into written language. this model is a synthesis of motor Bidirectional Encoder Representations From Transformers (BERT) representations. In order to develop its learning algorithm of word semantic representation, this model makes use of the BERT. In line with the language context, a comparison of models found that BERT outperforms the government foundation efficiency given by all the other models in the literature. Opinion mining is a branch of natural language processing that analyses public discourse to gauge public opinion on a product or topic. © 2023 ieee.

关键词： Emotion Recognition

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A Comprehensive Evaluation of Secured Electronic Voting System Design based on Face Biometric Authentication Policy 8

A Comprehensive Evaluation of Secured Electronic Voting Syst...

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8th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Potluri, Pandarinath Jayakarthik, R. Agarwal, Shivam Shobana, S. Venkata Padmavathi, S. Aarthi, R. Swarnandhra College of Engineering and Technology Cse Department Sitarampuram West Godavari district Narsapuram534275 India Saveetha College of Liberal Arts and Sciences Simats Dept of Computer Science India Gl Bajaj Institute of Technology and Management Department of Information Technology Gr. Noida India R.M.K Engineering College Department of It Chennai India Gst Gitam Deemed to be University Electrical and Electronics Engineering Hyderabad campus Telangana India R.M.D Engineering College Department of Ece R. S. M Nagar Gummidipoondi601206 India

ISBN: (纸本)9798350376425

In the field of electronic voting systems, ensuring secure and reliable authentication mechanisms is paramount to safeguarding the integrity and fairness of the democratic process. Face biometric authentication has emerged as a promising approach, integrating advanced deep learning algorithms for accurate and efficient verification of voters based on their facial features. this paper presents a comprehensive overview of the system architecture and design considerations for integrating face biometric authentication into electronic voting systems. It describes the hardware and software components essential for system functionality, discusses the selection of appropriate technologies for face recognition and encryption, and elaborates on the design of user interfaces, database structures, and ballot generation systems. Furthermore, the paper delves into the implementation of face detection and recognition algorithms, emphasizing the importance of anti-spoofing measures and multi-factor authentication mechanisms to enhance security. It also addresses voter registration processes, ballot generation, the voting process, and vote counting and verification procedures within the context of face biometric authentication. Additionally, the paper highlights the significance of robust security measures, including encryption techniques, access controls, and regular security audits, to mitigate cyber threats and ensure the integrity of the electoral process. By integrating face biometric authentication into electronic voting systems, this paper aims to contribute to the development of secure, transparent, and inclusive electoral systems, fostering trust and confidence in democratic governance, with an accuracy rate of 96%. © 2024 ieee.

关键词： Voting machines

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Hybrid BiLSTM-PSO Approach for Multi-Metering Point Day-Ahead Electrical Load Forecasting 8

Hybrid BiLSTM-PSO Approach for Multi-Metering Point Day-Ahea...

引用

8th international conference on Power Energy systems and Applications, ICoPESA 2024

作者： De Jesus, Noelyn M. Festijo, Enrique D. Apolinario, Gerard Francesco DG. Lopez, Dylan Josh Domingo Graduate School Technological Institute of the Philippines Manila Philippines Technological Institute of the Philippines Electrical Engineering Manila Philippines Chung Yuan Christian University Electrical Engineering and Computer Science Taoyuan Taiwan

ISBN: (纸本)9798350351668

Accurate individual load profile forecasting is essential for utilities to optimize resource allocation. A novel BiLSTM-PSO approach is proposed to address this challenge. By capturing short-term dependencies in load patterns, it significantly improves accuracy for each metering point. Historical data (BATELEC I, 2018-2023) is analyzed using statistical methods like Pearson correlation, cointegration, and Granger causality to identify these dependencies. A Bi-directional Long Short-Term Memory (BiLSTM) network is then optimized by Particle Swarm Optimization (PSO) for more accurate daily load profile forecasting. the BiLSTM-PSO approach achieves significant reductions in error metrics compared to the baseline model: 49.72% (R-squared), 17.95% (MAE), 17.42% (RMSE), and 30.88% (MSE). Outperforming alternatives, the Bi-LSTM approach offers superior forecasting accuracy. the results recommend integrating a BiLSTM-PSO model into information systems for real-time load forecasting, improving procurement planning and grid management efficiency for electric utilities. © 2024 ieee.

关键词： Particle swarm optimization (PSO)

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FPGA Implementation of Novel Triple Data Encryption Standard for High Speed Network Security Comparison with DES 8

FPGA Implementation of Novel Triple Data Encryption Standard...

引用

8th ieee international conference on Science, Technology, engineering and Mathematics, ICONSTEM 2023

作者： Siddhardha, M. Jagadeesh, P. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350347791

Aim: this research presents the implementation of Novel Triple Data Encryption Standard (Novel Triple DES) on Field-Programmable Gate Arrays (FPGAs) for high-speed network security. the proposed implementation is compared with the traditional Data Encryption Standard (DES) to demonstrate the improved security and performance of Novel Triple DES. Materials and Methods: the proposed Novel Triple DES implementation on FPGAs is tested using a Xilinx Virtex-7 device and compared to the traditional DES algorithm using simulation tools and performance metrics, such as encryption/decryption time and time delay analysis. the implementation requires a computer with VHDL synthesis tool and Modelsim software. the encryption and decryption processes are implemented in VHDL, while the key generation is executed using Modelsim. A time delay analysis and power consumption was conducted using 10 sample data sets, with an equal number of samples from two groups, and the results were compared using G-power with a 95% confidence interval. the research was conducted with alpha and beta set at 0.05 and 0.2. Results: In the proposed Novel Triple DES implementation, it was found that the time taken to encrypt a text data was 33.6 nanoseconds and the power consumed was 0.254 Watts. In comparison, the traditional DES algorithm had a time delay of 44.3 nanoseconds for text data encryption and consumed 0.467 Watts of power. the significance value obtained was 0.0249 which is (P © 2023 ieee.

关键词： Cryptography

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Comparative Analysis of Machine Learning Techniques for Mental Health Prediction 8

Comparative Analysis of Machine Learning Techniques for Ment...

引用

8th international conference on Communication and Electronics systems, ICCES 2023

作者： Naveen Paul, E. Juliet, Sujitha Karunya Institute of Technology and Sciences Department of Computer Science and Engineering TamilNadu India

ISBN: (纸本)9798350396638

the prevalence of mental health problems has prompted investigations into the use of machine learning to tackle the issues. Mental health is a crucial component of an individual's overall well-being and can be detected and treated early on, significantly improving the quality of life for those affected. this study examines the use of machine learning algorithms to predict mental health disorders using a dataset of self-reported information. Four commonly used machine learning models K-nearest neighbor classifier, logistic regression, random forest and decision tree are compared in terms of their performance. the objective of this study is to compare the performance of these machine learning algorithms on a self-reported mental health dataset and identify the most suitable model for predicting mental health. the challenges faced by the system include the limited size and quality of the dataset, the need for ethical considerations in handling sensitive mental health information, and potential biases in the *** results of the experiments identify the most suitable model for predicting mental health. © 2023 ieee.

关键词： Decision trees

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Interstitial Lung Disease Patterns Classification using Hybrid Features Set and Multi Level Segmentation Implemented by Machine Learning Algorithm 8

Interstitial Lung Disease Patterns Classification using Hybr...

引用

8th international conference on Communication and Electronics systems, ICCES 2023

作者： Verma, Binita Saraswat, Shweta Saraswat, Vrishit Misra, Rahul Jecrc University Department of Cse Rajasthan Jaipur India Arya Institute of Engineering and Technology Department of Cse Jaipur India Medanta Hospital Department Intervention Radiology India Agra College Department of Computer Application U.P Agra India

ISBN: (纸本)9798350396638

the respiratory disease causing the death by wrong analysis might have an impressive effect on the patient's health. In the pandemic asset limit has fundamentally postponed the standard conclusion of lung diseases patients including Interstitial Lung Disease (ILD). Early detection may improve the lives and health of patients. this paper proposed a methodology based on the Classifier using hybrid feature (statistical and structural) set extracted by superpixel image processing and fusion of K-means clusters on ILD images. Segmented super pixel images overcome the limitations of multiclass belongings. Semi-adaptive waveletbased fusion is applied over selected K-means clusters. Segmentation outputs then classified by Support Vector Machines (SVM). © 2023 ieee.

关键词： Support vector machines

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XNLI 2.0: Improving XNLI dataset and performance on Cross Lingual Understanding (XLU) 8

XNLI 2.0: Improving XNLI dataset and performance on Cross Li...

引用

8th ieee international conference for Convergence in Technology, I2CT 2023

作者： Kumar Upadhyay, Ankit Kumar Upadhya, Harsit JSS Academy of Technical Education Dept. of Computer Science and Engineering Bengaluru India JSS Academy of Technical Education Dept. of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798350334012

Natural Language Processing systems are heavily dependent on the availability of annotated data to train practical models. Primarily, models are trained on English datasets. In recent times, significant advances have been made in multilingual understanding due to the steeply increasing necessity of working in different languages. One of the points that stands out is that since there are now so many pre-trained multilingual models, we can utilize them for cross-lingual understanding tasks. Using cross-lingual understanding and Natural Language Inference, it is possible to train models whose applications extend beyond the training language. We can leverage the power of machine translation to skip the tiresome part of translating datasets from one language to another. In this work, we focus on improving the original XNLI dataset by re-translating the MNLI dataset in all of the 14 different languages present in XNLI, including the test and dev sets of XNLI using Google Translate. We also perform experiments by training models in all 15 languages and analyzing their performance on the task of natural language inference. We then expand our boundary to investigate if we could improve performance in low-resource languages such as Swahili and Urdu by training models in languages other than English. © 2023 ieee.

关键词： Natural language processing systems

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Cardiovascular Disease Prediction using ML Techniques to Find the Best Accurate Model 8

Cardiovascular Disease Prediction using ML Techniques to Fin...

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8th international conference on Communication and Electronics systems, ICCES 2023

作者： Reddy, Riya Kumari, Shweta Sardarmaran, P. Sathyabama Insitutte of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396638

Heart disease and cardiovascular disease are both illnesses that damage the heart. Correct functioning of the heart is very important for physical health. there are numerous types of cardio disease includes myocardial ischemia, cardiac arrest, congenital heart disease, myocardial infarction, peripheral heart disease, coronary heart disease, HCD. these types of diseases can be life threatening. It has been diagnosed that men experience more HCD compared to women. Also, it has been said that men diagnosed quick heart attacks than in women. It is the need to be early diagnosis of these diseases more efficiently and accurately. the goal of the study is to create and evaluate several ML algorithms for reliably predicting if a patient will have a cardiovascular ailment. the primary contribution is the evaluation of three well-known ML algorithms, namely Random Forest, Naive Bayes and Support Vector Machines, for predicting cardiovascular disease. When compared with all the results of various algorithms, Random Forest has the highest accuracy (97.94%). © 2023 ieee.

关键词： Support vector machines

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