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Exploring the Impact of Univariate Feature Selection Method on Machine Learning Algorithms for Heart Disease Prediction

Exploring the Impact of Univariate Feature Selection Method ...

引用

2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Islam, Mainul Islam, Rafiqul East West University Dept. of Computer Science and Engineering Dhaka Bangladesh Dhaka University of Engineering & Technology Dept. of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350316001

This study compares various machine learning techniques such as logistic regression, support vector machine (SVM), random forest, Naïve Bayes, and decision tree. Later, a feature selection approach that considers the Univariate Feature Selection (UFS) method was applied to select the best features based on statistical testing. In this research, we employed the ANOVA F-test to pick the most significant features, and we used a cutting-edge dataset for experimental analysis. The ANOVA F-test was used to exploit the target variable's dependency on the continuous variables. The results show that employing the UFS approach for picking features improves the performance of the algorithms. According to the experimental results, using UFS with the logistic regression technique yields 93.3% accuracy, whereas the standalone Naïve Bayes algorithm achieved the best accuracy. © 2023 IEEE.

关键词： Feature Selection

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Implementing HR Management System with an AI Powered ChatBot 3

Implementing HR Management System with an AI Powered ChatBot

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Susitra, D. Pratheesh, G. Karthiga, J.D. Dhanalakshmi, K. Shamreen Ahamed, B. Sylvia Grace, J. Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9798331527549

Human Resource (HR) management is one important section that increases the success of an organization to handle some important functions like employee data manipulation, attendance tracking, payroll processing, and performance evaluation. With the management of these processes becoming key to the success of small-scale startups, it poses a very big challenge for many of them to be able to manage these processes efficiently and cost efficient. By contrast, the old era of traditional HR systems was based on database driven architectures that are expensive in terms of operational cost, require storage space and complex infrastructural needs. These limitations often make traditional HR systems impossible for a startup that would prefer lightweight, affordable, and scalable solutions. Startups need robust platforms to streamline HR work streams, but often lack with financial resources for building complex database driven systems. In this paper, an AI powered HR management system is developed to simplify employee management processes for small scale startups. In this system. cache memory is used instead of traditional databases, thereby the operational cost of the system will be reduced for storage and retrieval of employee details. Automated Attendance Management enhanced with Face Recognition technology, Payroll Management, Leave Management, Feedback Submission modules are included. It is integrated with an AI powered chatbot, which facilitates a smooth interaction by the employees with query for information, submit leave requests and pull up all payroll information etc. A user-friendly dashboard for a consolidated view of employee profiles, attendance records, leave approvals, and payroll data can be served over an optimized and time efficient HR workflow. This system addresses the needs of the startups such as flexible usage, cost effective operations and improved resource management. © 2025 IEEE.

关键词： AI powered chatbot for Small-scale startups using cache memory attendance management face recognition HR Management System leave management payroll management

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Online Transit Entanglement Routing in Quantum Networks: Architecture Design and Optimization

Online Transit Entanglement Routing in Quantum Networks: Arc...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ho, Wan-Ting Fang, Shing-Yan Hsieh, Wei-Chia Cheni, Li-Feng Kuo, Jian-Jhih Tsai, Ming-Jer National Tsing Hua University Dept. of Computer Science Hsinchu Taiwan National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi Taiwan

ISBN: (纸本)9798350351255

Quantum networks (QNs) gradually gain significant attention due to their higher security compared with classical networks. Conventional approaches in QN routing often aggregate multiple requests into a batch before determining their routing paths. However, this approach may overlook the limited lifetime of qubits, resulting in critical decoherence. In this paper, we present a new online entanglement routing architecture with an online optimization problem and propose a novel [1, O(log |V|)]-competitive algorithm supporting online requests with admission control, aiming to maximize the number of admitted requests. Finally, extensive simulation results show that our algorithm can outperform the existing approaches by up to 98%. © 2024 IEEE.

关键词： Quantum entanglement

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Secure Forgery Detection in Android Malware Applications with Trust Analysis in Google Play 3

Secure Forgery Detection in Android Malware Applications wit...

引用

3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Sargunan, P. Yasodhei S, Umadevi Ifet College of Engineering Dept of Computer Science Engineering Villupuram India

ISBN: (纸本)9798350322842

With the widespread use of Android devices, the threat of malware applications has become a serious concern for both individuals and organizations. This research study proposes a novel methodology for safe forgery detection in malware applications for Android using machine learning and behavioral analysis techniques. The proposed methodology involves feature set extraction, pre-processing, and classification steps to accurately detect Android malware applications while minimizing false positives. Experimental results demonstrate that the proposed methodology outperforms existing state-of-the-art techniques in terms of accuracy, precision, recall, and F1 score. The methodology was also able to accurately detect a wide range of malware types, including previously unknown or zero-day malware. Our study highlights the importance of using advanced technologies and innovative approaches to detect and prevent Android malware, and suggests that future research could investigate additional machine learning algorithms and behavioral analysis techniques to improve detection accuracy. As the prevalence and sophistication of Android malware continue to increase, it is critical that researchers and practitioners work together to develop effective and reliable methods for detecting and preventing these threats. By doing so, users and organizations can be protected from the harmful effects of Android malware, and ensure the ongoing security and integrity of the Android ecosystem. © 2023 IEEE.

关键词： Feature extraction

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High-Performance Face Identification Using Randomly Selected Channels from Hyperspectral Images

High-Performance Face Identification Using Randomly Selected...

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Furusawa, Sota Premachandra, Chinthaka Graduate School of Engineering and Science Shibaura Institute of Technology Dept. of Electrical Engineering and Computer Science Tokyo Japan

ISBN: (纸本)9798350378214

With the recent development of information technology, the importance of protecting personal information has increased. Because of the vulnerability in passwords, biometric authentication is now being used as a method of personal information protection. However, biometric authentication has the possibility of malicious authentication due to the emergence of technology that can generate biometric data that is difficult to duplicate. Therefore, this study aimed to solve the security problems posed by Generative Adversarial Networks (GANs) in biometric authentication by using Hyperspectral Images (HSI). We searched for solutions to the problems identified in previous studies, such as the inability to correctly identify individuals who have not been previously learned and the length of time required for identification. In this paper, we used three channels of data randomly selected from hyperspectral face images for learning to identify individuals by binary classification into 'learned subject (class)' and 'unlearned subjects (classes)'. In this case, discriminators based on the least-squares generative adversarial network (LSGAN) model was developed for each class using three randomly selected channels of data from the HSI for two class classification. The proposed method significantly reduced the identification time and achieved very high discrimination accuracy in all classes. In addition, the results showed high accuracy throughout multiple face identification experiments. © 2024 IEEE.

关键词： Generative adversarial networks

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HGP-NLP at BioLaySumm: Leveraging LoRA for Lay Summarization of Biomedical Research Articles using Seq2Seq Transformers 23

HGP-NLP at BioLaySumm: Leveraging LoRA for Lay Summarization...

引用

23rd Meeting of the ACL Special Interest Group on Biomedical Natural Language Processing, BioNLP 2024

作者： Malik, Hemang Pradeep, Gaurav Seth, Pratinav Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Dept. of Data Science & Computer Applications India Dept. of Computer Science & Engineering India

ISBN: (纸本)9798891761308

Lay summarization aims to generate summaries of technical articles for non-experts, enabling easy comprehension for a general audience. The technical language used in research often hinders effective communication of scientific knowledge, making it difficult for non-experts to understand. Automatic lay summarization can enhance access to scientific literature, promoting interdisciplinary knowledge sharing and public understanding. This has become especially important for biomedical articles, given the current global need for clear medical information. Large Language Models (LLMs) (Yao et al., 2024), with their remarkable language understanding capabilities, are ideal for abstractive summarization, helping to make complex information accessible to the public. This paper details our submissions to the BioLaySumm 2024 Shared Task: Lay Summarization of Biomedical Research Articles (Goldsack et al., 2024). We fine-tune and evaluate sequence-to-sequence models like T5 across various training dataset settings and optimization methods such as LoRA (Hu et al., 2021b) for lay summarization. Our submission achieved the 53rd position overall.. ©2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Vote Bank Forecast Based on Social Media Sentiment Analysis 17

Vote Bank Forecast Based on Social Media Sentiment Analysis

引用

17th International Conference on Open Source Systems and Technologies, ICOSST 2023

作者： Zaland, Zubair Bazai, Sibghat Ullah Ghafoor, Muhammad Imran Akram, Saira Waseem, Muhammad Qaseem, Muhammad Dept. of Software Engineering Quetta Pakistan Dept.of Computer Engineering Quetta Pakistan Pakistan Television Corporation Lahore Pakistan Dept. of Engineering Pakistan

ISBN: (纸本)9798350381320

Sentiment Analysis of human behavior from social media revolutionized many fields, such as health, business, and election prediction. Sentiment analysis is the process of analyzing an individual's opinions, attitudes, views, sentiments, and emotions. In order to predict sentiments, predicting the polarity of words and categorizing them as positive or negative are the major goals. A quick and effective way to determine what the public thinks about one's own political party, the opposition party, and political figures is through Twitter. prior sentiment analysis techniques have primarily struggled with classification accuracy because biases in the training data cause a sizable portion of tweets to be incorrectly classified. As a remedy, we set out to collect real-Time data in order to forecast Pakistan's next election results. Users can quickly share real-Time updates on a variety of activities and events on Twitter, promoting the rapid diffusion of information and news. To do this, we accessed the Twitter API through the 'Tweepy'' Python package and retrieved real-Time data from the service. Through keyword searches, we were able to find more than 5,000 tweets. We attributed feelings to particular tweets using Python's 'TextBlob'' package. We are able to generate prediction graphics by determining the frequency of positive attitudes with respect to a particular candidate. © 2023 IEEE.

关键词： Forecasting

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Quantum-Enhanced Machine Learning for Precision Breast Cancer Diagnosis 17

Quantum-Enhanced Machine Learning for Precision Breast Cance...

引用

17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Banerjee, Agniva Indian Institute of Science Education and Research Bhopal Dept. Electrical Engineering and Computer Science Bhopal India

ISBN: (纸本)9798331531195

Cancer is the leading cause of premature mortality. Globally, the prevalence of cancer is on the rise, and early-stage diagnosis is crucial for cancer recovery and survival. However, the presence of circulating tumor cells (CTCs) and heterogeneity within the tumor makes it hard to track how cancer changes over time and how resistance to treatment develops. Unfortunately, the cancer diagnosis precision remains inadequate, endangering precious human lives and necessitating costly procedures for accurate treatment. Machine learning (ML) has proven capable of discovering meaningful information and making predictions, particularly with large datasets. Moreover, quantum computers (QCs) inherently possess superposition and parallel computation, features not found in classical computing. Researchers have demonstrated that quantum neural networks (QNNs) achieve a significantly better effective dimension than classical NNs. In this context, this study investigates the application of classical and quantum machine learning (QML) algorithms to classify breast cancer images. Region-based segmentation is used to identify regions of interest and image processing algorithms are performed on segmented breast cancer images to extract meaningful features (e.g., perimeter, area, compactness). An user interface (UI) based software is created to swiftly distinguish benign and malignant instances during image analysis. Although QNNs perform poorly than the classical NNs, there is still a long way to go in developing feature maps and techniques for better performance. In contrast, the classical NNs have gone a long way with well-established algorithms and packages available to us. This research shows that with further development, QML has the potential to enhance cancer diagnosis accuracy and speed, ultimately improving early detection and treatment outcomes. © 2025 IEEE.

关键词： Diseases

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An Improved Diagnostic Approach for Classifying Tomato Leaf Diseases using Ensemble Deep Learning based Technique 8

An Improved Diagnostic Approach for Classifying Tomato Leaf ...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Batra, Salil Chakraborty, Gouri Shankar Bortty, Joy Chakra Polok, Sourav Das Ahmed, Tamjid Taki, Tanvir Ahmed Lovely Professional University Dept. of Computer Science and Engineering Punjab Phagwara India Westcliff University Department of Computer Science IrvineCA United States

ISBN: (纸本)9798350367904

Tomato is one of the most common vegetables that has high commercial importance for abundant production to meet the huge demand world-wide. There are several challenges that put impact on the mass production of tomato, and which are required to be resolved timely. One of the major challenges is tomato leaf diseases where different types of diseases are there which need to be identified correctly to protect the plants, otherwise it can cause severe harm to the plants which ultimately effects the production of tomato. To have a solution, several scientists and researchers have been working on this to classify diseases in a very initial stage. Diseases of the plants can be classified from leaf image, but it's difficult to identify disease manually by observing through eyes. Deep learning techniques are being used to identify different types of disease using the leaf image. Several existing approaches have been discussed in this study where different detection processes have been proposed. But, in most of the cases, several limitations have been observed regarding the performance of those traditional frameworks. In this study, a novel disease detection approach has been proposed to detect multiple types of tomato leaf disease through analyzing the leaf images. Here, after combing the performances of four CNN models (MobileNetv2, ResNet50, VGG19, VGG16), an ensemble deep learning technique has been applied to identify different types of the tomato leaf disease as well as to classify the healthy and affected leaf. The performance achieved from the proposed ensemble framework ensures optimum accuracy of 96.7% with efficiency. A proper comparative analysis has been presented that supports the novelty and potentiality of the work. © 2024 IEEE.

关键词： Fruits

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Priority-Considered Channel Access for Registration-Backoff-Time (RBT) -based IEEE 802.11ah IOT Network 17

Priority-Considered Channel Access for Registration-Backoff-...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Huang, Chung-Ming Chen, Chia-Hsiang Ma, Jianhua National Cheng Kung University Dept. of Computer Science and Information Engineering Tainan Taiwan Hosei University Faculty of Computer and Information Science Tokyo Japan

ISBN: (纸本)9798350351637

This work devises a High Priority Reservation - centric Registered Backoff Time -based (HPR-RBT) channel access method to enhance the performance of IEEE 802.11ah IoT network. The proposed HPR-RBT method allows the STAs that have high priority's uplinked data to reserve, which is through the claiming action, the privilege of channel access. HPR-RBT adopts the Trigger RAW mode of IEEE 802.11ah and the RBT mechanism. In HPR-RBT, the Restricted Access Window (RAW) of IEEE 802.11ah is divided into (1) a claiming RAW that contains multiple claiming slots and (2) a data RAW that contains multiple data slots. Each claiming slot is further divided into many tiny slots, each of which is for one STA to claim its channel access's reservation for its high priority's uplinked data. Each data slot is further divided into a scheduled sub-slot, in which the channel access of the high priority's uplinked data is scheduled, and an unscheduled sub-slot, in which the channel access of the regular priority's uplinked data is allowed. Furthermore, this work proposes a grouping mechanism to balance the load of high priority's uplinked data across RAW's data slots to prevent excessive high priority's uplinked data in any particular RAW's data slot. Through the grouping mechanism, the IEEE 802.11ah Access Point (AP) can assign the data slot that has the lowest load of high priority's uplinked data to a newly associated STA of the AP. Comparing with the legacy 802.11ah method, the experiment result shows that the proposed HPR-RBT method has (i) the shorter transmission latency and (ii) the better throughput when the number of STA becomes bigger. © 2024 IEEE.

关键词： Internet of things

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