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Advanced Fuzzy Rule Set-Based Approach for Fetal Heart Rate Detection and Classification 4

Advanced Fuzzy Rule Set-Based Approach for Fetal Heart Rate ...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Ambilwade, R.P. Giri, Neeraj Nagaraju, Regonda Gupta, Vikas Kommina, Subhash Bhagavan Mishra, Devanshi National Defence Academy Department of Computer Science Pune India Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Malla Redy University School of Engineering Department of Ai & Ml Telangana Hyderabad India Gyan Ganga Institute of Technology & Sciences Dept of CSE- Aiml Jabalpur India It Department Andhra Pradesh India Ggits MP Jabalpur India

ISBN: (纸本)9798331518981

Fetal heart rate measurement is important in evaluation of fetal well-being and the visible sign of compromise or dysfunction. Congenital heart diseases rank among the main causes of infant mortality and can be identified by means of fetal heart rate monitoring. An effective and reliable method of recognizing and categorizing fetal heart rate is hence important for correct diagnosis and early intervention. The directed research utilizes an improved fuzzy rule set to boost fetal heart rate detection and classification precision. This technique incorporates fuzzy algorithms to adapt to the non-linearity of the fetal cardiotocographic signal as compared to precise methodologies. Some of the difficulties that arise when using FHR analysis include handling of maternal heart rates, power line interference and other biomass signals which tend to complicate the fetal signal. With fuzzy rule sets, this work shall be able to generate better classification between fetal and maternal components for accurate extraction and differentiation of FHR patterns. Moreover, this approach is able to account for uncertainties and temporal changes of the signal and could potentially provide a more robust solution for clinical applications of fetal monitoring and early detection of congenital heart diseases. © 2024 IEEE.

关键词： Fetal monitoring

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Ensuring Web Application Security: An OWASP Driven Development Methodology

Ensuring Web Application Security: An OWASP Driven Developme...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Sakthivel, M. Sivanantham, S. Bharathiraja, N. Bala Krishna, N. Kamalraj, R. Kumar, V. Saravana Sri Shanmugha College of Engineering and Technology Department of Artificial Intelligence and Data Science Salem Sankari India Saveetha Institute of Medical and Technical Sciences Department of Cse Saveetha School of Engineering Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Biomedical Engineering Chennai India Mohan Babu University School of Computing Department of Ai&ml Andhra Pradesh Tirupati India University School of Cs and It Jayanagar Bangalore India Sreenidhi Institute of Science and Technology Department of It Hyderabad India

ISBN: (纸本)9798350359688

Website security is a big threat for both large companies and individual developers, the more advanced the technology employed, the more challenging it is to build secure website development procedures. Hackers can easily access vulnerabilities that were not patched during development and that were thereafter distributed. This could result in significant financial losses for the business or the person. Users who visit vulnerable websites may be subject to XSS attacks that could compromise their systems, or an unsecure database configuration may result in a potential data leak, in which case the password of every user who has registered on the website is compromised. Users who use the same password on numerous websites run the danger of identity theft. This study offers the most common vulnerabilities that should be fixed in any application, giving developers the crucial place to start. The overwhelming number of vulnerabilities in each application that is being developed served as the impetus for this project. Every developer, regardless of expertise level, needs a place to start in order to fix any vulnerabilities that may have appeared in their program. The goal of this project is to create a safe online application in accordance with the recommendations of the Open Web Application Security Project (OWSAP). This demonstrates how web application vulnerabilities can be mitigated through coding changes, configuration adjustments, and patch applications. The top ten vulnerabilities according to OWASP are SQL injections, faulty authentication, sensitive data exposure, faulty access control, and Xml external entities. The web application's security has been examined and shown to have defenses in place for those vulnerabilities. © 2024 IEEE.

关键词： Information leakage

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Integration of MCC to detect disease in plants with DL Health Industry 4

Integration of MCC to detect disease in plants with DL Healt...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Bewerwal, Anubhav Habelalmateen, Mohammed I. Alsailawi, H.A. Venkatesan, D. Dhabli, Dharmesh Jabbar, Qasim Abdul Bader, Nabaa M. Alwan, Ali Saad Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Bhimtal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq University of Kerbala College of Medicine Department of Chemistry and Biochemistry Kerbala56001 Iraq St.Martin's Engineering College Dept.of Ai&ml Secundrabad India Vishwakarma Institute of Information Technology Pune India Alkunooze University College Medical Instrument Department Basra Iraq National University of Science and Technology College of Technical Engineering Dhi Qar64001 Iraq Kut University College Business Administration Department wasit Al kut52001 Iraq

ISBN: (纸本)9798350360165

Mobile gadgets are progressively assuming a vital role in individuals' everyday routines as they streamline several practical activities. Mobile cloud computing overcomes the constraints of memory, CPU, and battery life, and enhances its functionalities by integrating mobile and cloud computing. Big data analytics solutions facilitate the extraction of value from data by considering four key factors: volume, diversity, velocity, and validity. The study explores the use of mobile cloud computing and big data analytics in the context of networked healthcare. The focus is on the justification and development of interconnected healthcare applications and systems, highlighting the rise of cloud computing in this domain. The paper presents a cloudlet-based mobile cloud computing architecture designed specifically for big data applications in the healthcare sector. In the proposed framework the study assess two types of models that is cloud-based model and Wi-fi Cloudlet-based model, the working mechanism model describe the differentiation in processing. Furthermore, this study evaluates the results obtained and compares both mechanism based on latency in processing. © 2024 IEEE.

关键词： Electronic health record

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A Novel Approach to Avoid Road Traffic Accidents and Develop Safety Rules for Traffic Using Crash Prediction Model Technique 6th

A Novel Approach to Avoid Road Traffic Accidents and Develop...

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6th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2022

作者： Ahammad, Sk Hasane Sukesh, M. Narender, Mekala Ettyem, Sajjad Ali Al-Majdi, Kadhum Saikumar, K. Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Guntur India Department of Computer Science and Engineering Vaagdevi College of Engineering Telangana Warangal India Department of Computer Science and Engineering Jayamukhi Institute of Technological Sciences Narsampet Warangal India National University of Science and Technology Muscat Iraq Ashur University College Baghdad Iraq School of Engineering Department of AI-ML Malla Reddy University Telangana Hyderabad500043 India

ISBN: (纸本)9789811995118

The expansion of nations and communities has resulted in a variety of externalities, such as an increase in traffic accidents. Many attempts have been undertaken to minimize the injuries and fatalities and their intensity. Traffic safety modeling is a most significant technique to motivate harmless mobility because it is capable of the creation of Crash Prediction Models (CPMs) as well as the investigation of the fundamentals that contribute to the incidence of crashes. Statistical modeling has been utilized in this process in the past, regardless of the fact that they are aware of the limits of this sort of strategy which allows you to experiment with other options, such as using machine learning approaches. Machine learning approaches applied to collision datasets can assist researchers in better knowing the features of motorist behavior, highway surroundings, and meteorological circumstances that are linked to varying mortality risk levels. If we build a reliable predictive model capable of automatically classifying the degree of injury in diverse traffic accidents, we may be able to discover patterns involved in severe wrecks. These patterns of behavior and road accidents can be used to design traffic safety rules. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Performance Analysis of Various Machine Learning Techniques for Classifying Topic Categories from Text News Articles

Performance Analysis of Various Machine Learning Techniques ...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Ratnam Dodda Ahmed Shahebaaz Mohd Miskeen Ali Zeeshan Ahmed Mohammed Department of CSE (AI&ML) CVR College of Engineering Hyderabad India Department of CSE (Data Science) CVR College of Engineering Hyderabad India Sphoorthy Engineering College Hyderabad India University of the Cumberlands Burlington USA

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Text classification has gained significant importance in recent years, especially for document classification based on content. Documents typically contain unstructured data, which can be transformed into a structured format using specific algorithms. These documents are then assigned to one or more categories either manually or automatically through classification algorithms. Various classification algorithms are available, differing in speed and efficiency. E-news reports or articles are crucial sources of digital media, influencing various sectors. In our study, we focused on classifying news articles using several machine learning techniques: Support Vector Machine (SVM), Logistic Regression, Decision Tree, Random Forest, and Naïve Bayes classifier. This paper analyzes these techniques and identifies the best-performing algorithm for classifying news articles.

关键词： Support vector machines Logistic regression Machine learning algorithms Text categorization User interfaces Media Classification algorithms Performance analysis Naive Bayes methods Random forests

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Advancing Plant Disease Identification using Convolutional Neural Networks for Precise Diagnosis in Hibiscus Plants

Advancing Plant Disease Identification using Convolutional N...

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Artificial Intelligence and Emerging Technology (Global ai Summit), International Conference on

作者： Nandita Goyal Shivani Agarwal Tripti Choudhary Usikela Naresh Laith H. Alzubaidi Ram Sharma Department of Technology Technology Ajay Kumar Garg Engineering College Ghaziabad India Department of CSE-AIML Hi-Tech Engineering College Ghaziabad India Department of Computer Science and Engineering (AI&ML) CVR College of Engineering Hyderabad India The Islamic University Najaf Iraq Department of Computer Science and Engineering Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

The effectiveness and accuracy of deep learning models for the precise diagnosis of diseases in Hibiscus plants are studied. Gray mold, mainly caused by Botrytis cinerea, in diseased Hibiscus leaves is identified using 560 images collected to form the dataset. Preprocessing of the obtained dataset is performed through resizing, data augmentation, and noise reduction, which ensures the variability of the dataset. The preprocessed dataset is employed to train four different deep learning architectures: custom CNN, ResNet-50, VGG-16, and VGG-19. The benefits of using each model are that they are able to extract features and learn the pattern of the distribution of these features in the presence of the disease. The results are obtained based on the accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve for comparison purposes. According to the results, the best candidate is VGG-19, which demonstrates accuracy at the level of 98.54%, while the precision of 98.80%, recall of 98.50%, F1 score of 98.65%, and AUC-ROC of 99.00% are also high. The confusion matrices provide details from which the correct classification for VGG-19 of healthy and diseased leaves becomes evident. The real-time implementation of VGG-19 is completely reliable. The applied deep learning models provide numerous benefits in predicting the diseases in plants, and the usage of computational techniques will ensure food security and opportunities for current development.

关键词： Deep learning Training Plant diseases Accuracy Computational modeling Receivers Feature extraction Convolutional neural networks Reliability Artificial intelligence

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Elevating Digital Experiences by Enhancing Service Delivery through Revolutionary Cloud Services and Applications 7

Elevating Digital Experiences by Enhancing Service Delivery ...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Asha, V. Sreekanth, S. Nijhawan, Ginni Rajkumar, K. Varada Garg, Monica Hameed, Ali Abduhussien New Horizon College of Engineering Department of Computer Applications Bangalore India Institute of Aeronautical Engineering Dundigal Hyderabad500043 India Lovely Professional University Phagwara India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India Lloyd Law College Plot No. 11 Knowledge Park II Uttar Pradesh Greater Noida201313 India The Islamic University College of Medical Technology Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350350067

This research introduces an approach named 'Cloud-Driven Personalized Experience Enhancement' to satisfy the escalating demand for enhanced digital connectivity. Via cutting-edge cloud services and applications, the CPExE framework provides a groundbreaking solution for digital platforms. The framework is built upon three core components: Together with the Quantum-Inspired Data Analytics and Evaluation (QIDAE) and Personalized Content Recommendations (PRA), Dynamic Resource Allocation and Adaptation (DRAA) completes the framework. By leveraging quantum-inspired approaches, QIDAE analyzes vast datasets with outstanding effectiveness and exactness improving processes for data handling and personalization. This strategy allows for swift detection of patterns while making certain that data-driven understandings are both on time and precise. By changing cloud resources according to user requests the DRAA module guarantees uninterrupted access to computing power and data storage. By managing resources effectively in real-time it decreases latency and improves service speeds. By using complex algorithms to assess user habits and choices the PRA system boosts the digital experience. PRA uses both content-focused filtering and collaborative screening strategies to provide users with engaging content that matches their unique preferences. By applying sophisticated analytics and resource management methods with content personalization CPExE boosts the user experience and develops cloud technology efficiency. This framework signifies an important progress toward developing dynamic and tailored online environments. © 2024 IEEE.

关键词： Cloud platforms

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Threat Eye: Behavior Analytics for Cloud Security using ml

Threat Eye: Behavior Analytics for Cloud Security using ML

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International Conference on ent Computing and Control Systems (ICICCS)

作者： J. R. V. Jeny S. Shivaspandana K. Pavan N. Muthukumaran K. Akash Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Cloud computing has developed for its storage and accessibility of data, providing flexibility, scalability, and affordability. But its extensive use has also resulted in cyberattacks and unauthorized access. The traditional intrusion detection mechanisms become ineffective for different attack patterns, insider attacks and zero-day exploits. This paper provides an efficient way to address this issue by providing a mechanism to identify the threat based on the behaviour of the user. Unlike traditional approaches this paper provides a system that monitors the user behaviour patterns considering the features such as login history, authentication attempts, session duration, file access activities, password change frequency and IP location modifications. By considering these features the attack can be detected, and the data access is restricted to prevent from unauthorized access. This paper provides handling of attack with a combination of machine learning algorithms including SVM, Decision tree and XBoost, these help in distinguishing between the attacker and the authorized user. This helps in identifying complex attack behaviours and detect insider threats. Additionally, RSA encryption is integrated to secure data transmissions, preventing unauthorized access. By combining behavioural analysis, attacks prediction, and encryption, this provides a robust cybersecurity framework.

关键词： Support vector machines Logistic regression Machine learning algorithms Cloud computing security Scalability Intrusion detection Passwords Threat assessment Encryption Monitoring

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A Review On: Automated Surveillance System

A Review On: Automated Surveillance System

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engineering Informatics, IEEE

作者： Neha Thotapalli Raghava Rao Neerukattu Ravi Kumar Poluru Department Of CSE(AI&ML) G.Narayanamma Institute Of Technology & Science (For Women) Hyderabad India Department of Information Technology Institute Of Aeronautical Engineering Hyderabad

ISBN: (数字)9798350338522

ISBN: (纸本)9798350338539

Research involving human surveillance is crucial for addressing safety concerns. The growing need for safety in a variety of settings has sparked a flurry of activity in the field of surveillance technology research and development in recent years. Most current surveillance systems rely on monocular cameras, which are limited in their ability to offer three-dimensional depth information for person detection and tracking due to their fixed field of view. The growing need for safety in public places like schools, hospitals, airports, and malls has made automated video surveillance systems a hot topic of study in recent years. Detecting an object, then classifying it so that it may be tracked later, has become a standard feature of video surveillance. Target identification in successive video frames can be achieved using object tracking, although detecting an item might be challenging due to factors such as lighting changes, ambient factors, pose changes, occlusion, motion blur, and so on. Night vision detection is a challenging procedure that is nonetheless crucial in the battle against crime, public safety, and the efficient management of day-to-day labour. One difficult issue in the ever-changing world of video surveillance is the detection of abandoned objects in public areas. The suggested method employs background reduction and morphological filtering to recognize unattended items in a live setting. The discovery of the abandoned suspicious object and the timely notification of the appropriate authority is our primary objective.

关键词： Target tracking Reviews Systems architecture Video surveillance Rendering (computer graphics) Real-time systems Recording

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Activity Monitoring Tool for Windows using Keystroke Logging

Activity Monitoring Tool for Windows using Keystroke Logging

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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： Sai Teja Sajja Vivek Vardhan Asam M. A. Jabbar Computer Science Engineering Vardhaman College of Engineering Telangana India CSE (AI&ML) Vardhaman College of Engineering Telangana India

In recent years information and communication technologies has become an important part of human life. Most of the organizations facing challenges in maintaining the information secured within an organization because most of the information leakage is happening due to the employees in organizations. We thought of building a tool which can monitors the employee's activities without manually monitoring the employees. This paper mainly focuses to propose a real time key logger for monitoring the activities of employees within an organization. The real time key logger such as Activity Monitoring tool monitors the employee activities based on the Key logs, Screenshots, Microphone, System and Network inputs information. Tracked employee's activities information is stored in the employee system in the form of log files based on type of the log files these files sent to admin over email. So that Employee's activities can be monitored easily by looking over those files and admin can take the certain actions based on the organization terms and conditions to maintain security within organization effectively.

关键词： Buildings Organizations Real-time systems Windows Information and communication technology Security computer crime

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