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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Goyal, Manish Kumar Yadav, Dhananjay Kumar Brar, Khushmeen Kaur Mittal, Manisha Ojha, Ananta Alzubaidi, Laith H. Pathani, Ashish Vivekananda Global University Dept. of Computer Science & Application Jaipur India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chandigarh University Dept. of Electronics and Communication Engineering Mohali India Guru Tegh Bahadur Institute of Technology New Delhi India Dept. of Computer Science and Information Technology Bangalore India The Islamic University Najaf Iraq Uttaranchal Institute of Technology Uttaranchal University Department of Civil Engineering Dehradun India

ISBN: (纸本)9798331518592

The bacteria Mycobacterium tuberculosis is responsible for the infectious illness tuberculosis (TB). Although the lungs are the primary organs affected, the kidneys, bones, and brain may also be affected. Whenever an infected person coughs, sneezes, or talks, small droplets comprising the bacteria are released into the air and spread through the airborne system. The elimination of tuberculosis (TB) remains a problem due to factors such medication resistance, co-infection with HIV, and scarce resources in high-burden configurations, even with substantial efforts to treat the disease. As a major worldwide health concern, tuberculosis (TB) necessitates quick and precise diagnosis in order to be effectively managed. This study aimed to develop a TB detection model using chest X-ray images obtained from ***, utilizing Google's Collaboration Platform. The model was trained to recognize patterns within the TB chest X-rays to efficiently recognize TB within patients in order to be treated in time. We introduce a technique for transfer learning that proves preferable to the traditional methodology of adopting ImageN et weights. The hybrid methodology utilizes best first search technique in fusion with ResNet 50 model. The SVM classifier is used for categorizing the classes. An accuracy of 93.91 % is attained. © 2024 IEEE.

关键词： Deep learning

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Predictive Analysis of Cardiovascular Disease using Machine Learning Techniques 7

Predictive Analysis of Cardiovascular Disease using Machine ...

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7th International Conference on Image information Processing, ICIIP 2023

作者： Sreekruti Vijh, Surbhi Kumar, Sumit Gupta, Neetu Roy, Nihar Ranjan Amity University Department of Computer Science & Engineering Uttar Pradesh Noida India Sharda University Department of Computer Science & Engineering Uttar Pradesh Greater Noida India Amity University Department of Information Technology Uttar Pradesh Noida India

ISBN: (纸本)9798350371406

Cardiovascular or Heart diseases encompass a range of medical condition that impact the heart and blood vessels. The prediction of heart disease pose considerable complexity and present a formidable challenge within the medical field. According to the World Health Organization (WHO), cardiovascular diseases are the leading cause of death worldwide, claiming approximately 17.9 million lives each year, accounting for around 31% of all global deaths. In the United States, cardiovascular disease is responsible for an estimated 655,000 deaths annually, making it the leading cause of death, accounting for approximately 1 in every 4 deaths. To address the need for accurate prediction of heart disease, extensive research has employed advanced machine learning models. This study conducted a comprehensive evaluation of seven distinct classification models, combining various physiological factors with well-known machine learning algorithms. The model employed in this study includes Naïve Bayes, Logistic Regression, Decision Trees, Random Forest, XGBoost, CatBoost, and Voting Classifiers. Through the utilization of these models, a robust heart disease prediction system is designed, facilitating precise evaluation of an individual's risk. The observation shows that through meticulous evaluation and comparison, the Random Forest algorithm, which is a bagging technique, outperforms the existing state-of-the-art methods. It exhibited remarkable accuracy, yielding prediction results of approximately 90.16%. This exceptional accuracy establishes the random forest algorithm as the pre-eminent model for precise and reliable heart disease prediction within the scope of this study. © 2023 IEEE.

关键词： Predictive analytics

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification 13

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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13th IEEE Symposium on computer Applications and Industrial Electronics, ISCAIE 2023

作者： Wandy, Wandy Adi, Kusworo Ayu, Media Anugerah Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science South Jakarta Indonesia

ISBN: (纸本)9798350347319

Sports science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport science recognizes and prevents injuries. Sensors and statistics formalize Sports science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM2.5 and PM10). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports information system. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM2.5 and PM10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results. © 2023 IEEE.

关键词： Classification (of information)

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A Holistic Approach for Cyber Security Vulnerability Assessment Based on Open Source Tools: Nikto, Acunitx, ZAP, Nessus and Enhanced with AI-Powered Tool ImmuniWeb

A Holistic Approach for Cyber Security Vulnerability Assessm...

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sciences and Techniques of Automatic Control and computer Engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Azeddien M. Sllame Tayma Esam Tomia Ruqia Mohammed Rahuma Computer Network Department Faculty of Information Technology University of Tripoli Libya

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372649

This paper describes a cyber security vulnerability assessment methodology powered by AI tactics. The AI-powered tool “ImmuniWeb AI” effectively supported the tools of the suggested approach by automating a number of vulnerability management process tasks, including scanning, evaluation, prioritizing, and remediation. The recommended methodology makes use of the Nikto, Acunitx, Nessus, and ZAP proxy tools, which have been improved by the ImmuniWeb AI online tool, to quickly and effectively scan target systems in order to identify any vulnerabilities. Using such a variety of tools with a wide range of expertise practically produced a comprehensive method that can identify system vulnerabilities and generate well-written reports with appropriate remedy recommendations depending on the vulnerabilities' scores and level of danger. As a result, the outcomes showed how effective the suggested methodology.

关键词： Instruments Conferences Artificial intelligence computer crime Task analysis

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Artificial intelligence-driven prediction system for efficient management of Parlatoria Blanchardi in date palms

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Multimedia Tools and Applications 2025年第15期84卷 15293-15329页

作者： Hessane, Abdelaaziz El Youssefi, Ahmed Farhaoui, Yousef Aghoutane, Badraddine Abdellaoui Alaoui, El Arbi Nayyar, Anand STI Laboratory T-IDMS Faculty of Sciences and Techniques Moulay Ismail University of Meknès Errachidia52000 Morocco IA Laboratory Department of Computer Science Faculty of Sciences Moulay Ismail University of Meknès Meknes50000 Morocco IEVIA Team IMAGE Laboratory Department of Sciences ENS Moulay Ismail University of Meknès Meknes50000 Morocco Graduate School Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam

Pest management plays a pivotal role in ensuring sustainable crop productivity. Integrated Pest Management systems (IPMS) gaining importance due to the need for more sustainable agricultural methods. However, implementing standard pest management practices on a large scale is challenging due to their time-consuming and labor-intensive nature. In order to enhance pest management efficiency in date palm cultivation, this research paper proposes an Artificial Intelligence-driven Pest Prediction system, explicitly targeting the Parlatoria Date Scale (PDS), a significant pest affecting date palms. The approach leverages climatological factors to predict the occurrence of PDS nymphs and adult females. To address the challenges of data scarcity and imbalance, the Synthetic Minority Over-Sampling for Regression using Gaussian Noise (SMOGN) method is employed, which significantly enhances the dataset's utility for training machine learning models. The interpretability of these models is further enhanced using SHAP values, providing insights into the driving factors behind the predictions and facilitating trust and understanding in the AI-driven decision-making process. The Stacking-based Weighted Ensemble model stands out among the evaluated ensemble-based methods, achieving R2 scores of 77%, 88%, and 90% for predicting PDS nymphs, adult females, and total population incidence, respectively. This research highlights the potential of AI in revolutionizing Date Palm Integrated Pest Management systems (IPMS). It emphasizes the importance of model interpretability in adopting AI solutions to enhance the precision and effectiveness of pest management practices, thereby promoting a more sustainable agricultural system. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Machine learning

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VADDOT: Vocal Analysis to Detect Diseases of Throat 2

VADDOT: Vocal Analysis to Detect Diseases of Throat

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

作者： Senapati, Ipsita Pradhan, Animesh Senapati, Lipsita Prasath, Rajendra Swarnkar, Tripti Siksha '0' Anusandhan University ITER Department of Computer Science and Engineering Odisha Bhubaneswar India Mercedes Benz Research and Development India Karnataka Bengaluru India Indian Institute of Information Technology Faculty of Computer Science and Engineering Andhra Pradesh Sri City India

ISBN: (纸本)9798350344882

Pharyngeal diseases, or throat diseases, are rampant- owing to pollution, allergies, and pathogens. The current diagnostic practices for such diseases are invasive, time-taking, and prone to subjectivity. Aiming to develop a patient-friendly mechanism for easy and efficient detection of throat diseases, reducing the time taken for diagnosis, VADDOT describes a throat disease identification system which utilises machine learning to detect diseases based on vocal parameters. It aims to provide an alternative, noninvasive way for the early detection of various throat diseases. VADDOT consists of Data Collection Module (DCM), Feature Learning Module (FLM), and Machine Learning Model (ML). DCM collects vocal samples by asking the concerned patients to record their voices while pronouncing various phonetics. FLM finds vocal parameters such as frequency, intensity, jitter, pitch, etc. from the voice. The Machine Learning Model works on Support Vector Machine trained by vocal parameter datasets of various throat diseases. Research in this field has shown promising results on diseases such as Parkinson's, Alzheimer's, and COVID-19. Since, the research is relatively new, datasets available to train the machine learning model are limited, causing difficulties in widening the spectrum of diseases that can be successfully detected through VADDOT. It is non-invasive, affordable, easy to use and reduces the diagnosis time. With further development and testing, the system has the potential to be widely used in healthcare facilities, enabling early detection and treatment of diseases, ultimately leading to better patient outcomes. It is simple and requires minimal training, making it an accessible tool for healthcare providers. Accuracy scores of 90.3% (Parkinson's Disease) and 97% (COVID-19) have been achieved so far by VADDOT. © 2023 IEEE.

关键词： COVID-19

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A Comparative Study of Access Control Models for Ubiquitous Computing systems 23

A Comparative Study of Access Control Models for Ubiquitous ...

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Proceedings of the 5th International Conference on information Management & Machine Intelligence

作者： Sayali Renuse Parikshit N. Mahalle Gitanjali Rahul Shinde Nilesh P. Sable Department of Computer Engineering Vishwakarma Institute of Information Technology India Department of Artificial intelligence and Data science Vishwakarma Institute Of Information Technology India Computer Science Engineering Vishwakarma Institute of Information Technology India Information Technology Vishwakarma Institute of Information Technology India

ISBN: (纸本)9798400709418

The Internet of Things (IoT) and Machine-to-Machine (M2M) communication have connected devices, enabling major advances in various fields. The priority now is secure and efficient management of these interconnected systems. IoT ecosystems need access control to manage interactions. Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), Policy-Based Access Control (PBAC), and Context-Based Access Control (CBAC) are examined in the context of Industrial IoT (IIoT) in this paper. Intelligent manufacturing and anticipatory maintenance are among the many opportunities created by IoT and IIoT rapid growth. However, these advances present new security and access control challenges. Access control is essential for managing user, device, and application authorization in complex, interconnected environments. Our goal is to analyze their suitability, pros, and cons in industrial settings where strict access control is needed to ensure system safety, confidentiality, and efficiency. The IIoT case study implementation of these access control mechanisms is the focus of this paper. We examine an IIoT scenario in which a manufacturing plant has a network of machinery, sensors, and control systems. The case study shows how RBAC, ABAC, PBAC, and CBAC apply pragmatically. We evaluate their ability to manage access to critical machinery, data, and devices. We analyze and compare these access control mechanisms within the IIoT framework to determine the best one for the industrial environment, taking into account scalability, security, real-time decision making, and industrial process complexity. This paper examines access control mechanisms and practical observations from a real-world IIoT case study to improve IIoT security discussions. The findings can help IIoT practitioners, researchers, and decision-makers choose access control solutions. Industrial operations can become safer and more efficient.

关键词： Access Control

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Implementation of Audio Signals Denoising for Perfect Speech-to-Speech Translation Using Principal Component Analysis

Implementation of Audio Signals Denoising for Perfect Speech...

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2023 International Conference on science, Engineering and Business for Sustainable Development Goals, SEB-SDG 2023

作者： Julius, Olaniyan Obagbuwa, Ibidun. C. Adebiyi, Ayodele A. Michael, Esiefarienrhe B. College of Pure and Applied Sciences Landmark University Department of Computer Science Kwara-State Omu-Aran Nigeria School of Natural and Applied Sciences Sol Plaatje University Department of Computer Science & Information Technology Kimberley South Africa North-West University Department of Computer Science Mafikeng South Africa

ISBN: (纸本)9798350324785

The accuracy of any speech translation system essentially depends on the quality of the audio signal inputted into it. Many researchers have worked on different approaches in an attempt to reduce the level of noise in audio signals. Such approaches, among others, include Wavelet, Fourier Transform (FT), and deep learning. These algorithms worked well on noisy speech to a certain degree, but their degree of accuracy is not sufficient enough for speech-to-speech (S2S) translation because the presence of just a little noise in the signal can alter the semantic representation of the underlying language. Since it is nearly impossible for any of this single algorithm to produce a perfect (noiseless) signal, this paper presents a layered approach for total noise removal by stacking Principal Component Analysis (PCA) on Short Time Fourier Transform (STFT). In this approach, a band-pass channel is created using STFT, which reduces the signal noise level to the barest minimum while the residual noise is completely removed by performing PCA on the refined signals. Experimental results clearly showed that this approach almost doubles the signal-to-noise ratio (SNR) of the output signal for all the 10 audio samples being tested, thus making it relatively than the aforementioned approaches in terms of quality of outputs, and suitable for accuracy-sensitive domains such as speech-to-speech translation system development. © 2023 IEEE.

关键词： Principal component analysis

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The Empirical Evaluation of Artificial Intelligence-based Techniques for Improving Cyber Security 2

The Empirical Evaluation of Artificial Intelligence-based Te...

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2nd International Conference on Electronics and Renewable systems, ICEARS 2023

作者： Nayak, Chhaya Rao, Chintala Lakshmana Alam, Tanweer Singh, Shalini Islam, Shaziya Maginmani, Umme Habiba Pimpri Chinchwad College of Engineering Pune India School of Law Visakhapatnam India Islamic University of Madinah Faculty of Computer and Information Systems Department of Computer Science Madinah Saudi Arabia Graphic Era Deemed to Be University Department of Ece Uttarakhand Dehradun248002 India Rungta College of Engineering and Technology Bhilai India Secab Institute of Engineering and Technology Department of Computer Science and Engineering Karnataka Vijayapur India

ISBN: (纸本)9798350346640

Artificial intelligence (AI) technologies have been recently developed, and their advantages can be observed in a wide range of domains, from image processing to face detection. AI-based approaches can assist opponents to enhance their attack strategies while also improving cyber protection technologies. The efficiency of AI techniques against cyber security issues are evaluated. Here, the primary data and a quantitative study design approach is used. The information is then obtained from software industry professionals. The sample size for this study is 549 people, and it included confirmatory factor analysis, discriminant validity testing, model fundamental analysis, and hypothesis evaluation. All variables' P-values were found to be statistical, with the exception of intelligent agents, which had no meaningful relationship to AI or cyber security. The main issues were availability, geographical locations, study population, and other related variables. © 2023 IEEE.

关键词： Factor analysis

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Metric-Based Resolvability of Quartz Structure

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computers, Materials & Continua 2022年第4期71卷 2053-2071页

作者： Muhammad Imran Ali Ahmad Muhammad Azeem Kashif Elahi Department of Mathematical Sciences United Arab Emirates UniversityAl AinUnited Arab Emirates College of Computer Science&Information Technology Jazan University JazanSaudi Arabia Department of Aerospace Engineering Faculty of EngineeringUniversiti Putra MalaysiaMalaysia Deanship of E-learning and Information Technology Jazan UniversityJazanSaudi Arabia

Silica has three major varieties of crystalline. Quartz is the main andabundant ingredient in the crust of our earth. While other varieties are formedby the heating of quartz. Silica quartz is a rich chemical structure containingenormous properties. Any chemical network or structure can be transformedinto a graph, where atoms become vertices and the bonds are converted toedges, between vertices. This makes a complex network easy to visualize towork on it. There are many concepts to work on chemical structures in termsof graph theory but the resolvability parameters of a graph are quite advanceand applicable topic. Resolvability parameters of a graph is a way to getting agraph into unique form, like each vertex or edge has a unique identification bymeans of some selected vertices, which depends on the distance of vertices andits pattern in a particular graph. We have dealt some resolvability parametersof SiO2 quartz. We computed the resolving set for quartz structure and itsvariants, wherein we proved that all the variants of resolvability parameters ofquartz structures are constant and do not depend on the order of the graph.

关键词： Quartz polycyclic aromatic hydrocarbon related structure metric dimension metric or distance-based resolvability parameters

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