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Natural Language Processing for Generating Synthetic News Detection

Natural Language Processing for Generating Synthetic News De...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Bediga Sharan Umar Mubarak Mulani R. Naveenkumar Sujit Kumar Badodia Hemant Makawana Joshuva Arockia Dhanraj School of Computer Science & Artificial Intelligence SR University Warangal Telangana India Computer Engineering KJ College of Engineering and Management Research Pune Maharashtra India Department of Computer Science and Engineering Chandigarh Colleges of Engineering Chandigarh Group of Colleges Jhanjeri Mohali Punjab India Devi Ahilya Vishvavidyalaya Institute of Engineering and Technology Indore Madhya Pradesh India Department of Computer Science and Engineering (AI&ML) School of Engineering Dayananda Sagar University Devarakaggalahalli Bengaluru Karnataka India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Synthetic news production, language translation, and text summarization have all seen significant success with sophisticated text generating techniques. Although neural model-generated false news detection has advanced recently, the results are not easily transferable to the efficient identification of misinformation created by humans. The significant contrasts between fake news produced by machines and those written by humans, including style and underlying intentions, are what prevents them from successfully transferring. In light of this, we provide a brand-new framework for creating training cases that draw inspiration from the well-known techniques and styles of propaganda written by people. But these methods might be misused to spread false information. Researchers create superior news material using a unique generation technique called FACTGEN in order to gain a greater understanding the possible risks associated with synthetic news. Because most text generation techniques now in use either provide very little more information or break continuity across the inputs and outputs, the synthetic news becomes less reliable. The results of experiments conducted on real-world datasets show that FACTGEN generates news items that are bursting with data and accurate.

关键词： Training Translation Accuracy Text summarization Production Natural language processing Reliability Fake news Informatics

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The Use of 6G Communication Technology in Healthcare Applications for the Accurate and Better Transmission

The Use of 6G Communication Technology in Healthcare Applica...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Jamvant Omkar S. Menaka J Praveena Neeraj Varshney B. Arunkumar G Vinoda Reddy Department of Electronics & Communication Engineering IES College Of Technology Bhopal M. P India Nehru Institute of Information Technology and Management Coimbatore Department of Science and hummanities Prince Shri Venkateshwara Padmavathy Engineering College TN Department of Computer Engineering and Applications GLA University Mathura Computer Science Engineering department Karpagam Academy of Higher Education Department CSE (AI&ML) CMR Technical Campus Medchal Hyderabad

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

The IIoT is the most important element in the 6th generation networks, or 6G, so it greatly depends on effective data processing and storage. For the cause of ensuring smooth node-to-node communication, 6G provides ultra-low delays, high bandwidth, and higher-level QoS. Intelligent diagnosis, numerous in-hospital, and distant healthcare services are prerequisites to enable healthcare institutions to provide patientcentered care. What is essential is that the system hosting an array of uses, ranging from ambient appropriate living settings with wearable technology-enabled remote patient monitoring to robotic procedures powered by an intelligent networking platform. Therefore, this work addresses the needs of the healthcare sector by providing a reference-layered contact architecture for locations and devices in real-time communication. The unique 6G technology can, therefore, be utilized with great foresight to the future in the healthcare sector, enabling effective treatment and intelligent service integration. Because several networking nodes, including terminal devices, are integrated into effective health service delivery, a backbone structure is thus required for the services that take much time to complete. Therefore, a backbone structure for managing services is required to enable smooth communication by the framework, developed to enable this kind of 6G-enabled advanced healthcare application.

关键词： 6G mobile communication Patient monitoring Surgery Medical services Quality of service computer architecture Transforms

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Securing Wireless and Optical Networks: Advanced Strategies for Network and Information Security in Modern Communication Systems

Securing Wireless and Optical Networks: Advanced Strategies ...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Gulshan Dhasmana Vishal Sharma Nandini Shirish Boob Aravind K R. Akhilesh Reddy Kanchan Yadav Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Lovely Professional University Phagwara India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu Department of Electronics and Communication Engineering New Horizon College of Engineering Bangalore Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana Department of Electrical Engineering GLA University Mathura

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

In the era of rapidly changing communication systems, securing wireless and optical networks has become of essence to protect sensitive data and ensure operation. This study addresses network and information security in current communication systems by studying cutting edge techniques to enhance network and information security in wireless and optical networks. The wireless networks are convenient and may be moved around, but they are also prone to hacking, illegal access or some other cyberattacks. As optical networks possess both a large capacity and relatively low latency, it is not without special security issues such as fibre tapping and signal jamming. This study presents detailed analyses of state of the art authentication techniques, intrusion detection systems (IDS), and encryption algorithms specifically designed for these networks. Then there are, such as Quantum Key Distribution (QKD) which almost assuarly unbreakable techniques are examined alongside conventional cryptographic methods. Additionally, anomaly detection based on machine learning is explored for real time threat identification in optical and wireless channels. It stresses the need of the cross domain solutions and multi layered security frameworks covering the network layer and the physical security measures. Through case studies and recent developments in cyber threats and network resilience, this article provides a thorough understanding of how these tactics could be used to make networks such as wireless network and optical network more resilient against increasing cyber risks.

关键词： Optical fibers Wireless sensor networks Wireless networks Intrusion detection Machine learning Optical fiber networks Network security Quantum key distribution Quantum cryptography Resilience

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Automated Apple Wax Detection with Convolutional Neural Networks

Automated Apple Wax Detection with Convolutional Neural Netw...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： K Sundravadivelu S Dheenath M Sakthimohan G Elizabeth Rani M. Thenmozhi Department of Computer Science Madurai Kamaraj University Madurai Tamil Nadu India Department of AI&DS KalaignarKarunanidhi Institute of Technology Coimbatore Tamil Nadu India Department of ECE KalaignarKarunanidhi Institute of Technology Coimbatore Tamilnadu India Department of CSE (AI&ML) KalaignarKarunanidhi Institute of Technology Coimbatore Tamilnadu India Department of AI&DS Sri Eshwar College of Engineering Coimbatore Tamilnadu India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

In this work, the application of wax coatings on fruits is a widespread practice aimed at enhancing visual appeal and shelf life but raises concerns regarding consumer health and transparency. This study introduces an automated approach using IoT and machine learning to detect wax coatings on fruits. Leveraging Convolutional Neural Networks (CNNs), we developed a robust system capable of accurately distinguishing between waxed and unwaxed apples. Our methodology involved collecting a comprehensive dataset, preprocessing images, and training the CNN to classify images based on wax presence. The system demonstrates high accuracy, validated through rigorous testing and comparison with existing methods. This research contributes to enhancing consumer transparency in food safety and sets a foundation for future applications in agricultural technology.

关键词： Training Visualization Accuracy Machine learning Aerospace electronics Food safety Convolutional neural networks Coatings Testing

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Machine Learning Mastery in Cardiovascular Risk Assessment

Machine Learning Mastery in Cardiovascular Risk Assessment

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Suma Kamalesh Gandhimathi Rajesh S M K Ghamya A. Lakshmi Lohitha A. Vinitha C. Surya Prakash Reddy School of Computer Science and Engineering VIT-AP University Amaravathi India Department of CSE GITAM School of Technology GITAM University-Bengaluru Campus Bengaluru India Department of CSE(AI&ML) School of Computing Mohan Babu University Tirupati India Department of CSE Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Coronary illness stays a basic general well-being concern, requiring progressed prescient instruments for opportune determination and intercession. It is the maincause of death universally, liable for almost 18million passings every year. Early recognition and intercession are significant for lessening CVD mortality. ai has arisen as an integral asset for anticipating coronary illness risk, offering expected benefits for customized medication and further developed medical service results. Data preprocessing, feature selection, model training, and evaluation are all covered in this review of ml-based heart disease prediction systems. We talk about the logistic regression, decision trees, random forests, support vector machines (SVMs), naive Bayes, k-means, and artificial neural networks (ANNs) ml algorithms used to predict heart disease. We additionally feature the significance of interpretability and logic in ml models for clinical navigation. At last, we talk about future directions in ml-based coronary illness expectation, accentuating the capability of deep learning and hybrid models for upgrading prescient precision and clinical importance.

关键词： Heart Support vector machines Machine learning algorithms Prevention and mitigation Predictive models Prediction algorithms Data models Bayes methods Random forests Diseases

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Heart Disease Prediction using Machine Learning

Heart Disease Prediction using Machine Learning

引用

Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： E.N.Vijaya Kumari Shaik Jakeer Hussain T. Srinivas Reddy M.Ratna Sirisha Manohar Madgi Kora Swetha Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Department CSE (AI & ML) Institute of Aeronautical Engineering Hyderabad Department of ECE Malla Reddy Engineering College Medchal Telangana Department of Computer Science and Engineering (CSE) CVR College of Engineering Hyderabad School of Computer Science and Engineering KLE Technological University Hubballi Karnataka India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

A complex disease, heart disease affects many people worldwide. Fast and accurate cardiac disease detection is crucial in cardiology. This article proposes a machine learning-based cardiac disease diagnosis method that is efficient and accurate. The system utilizes classification algorithms like SVM, LR, ANN, K-nearest neighbor, NB, and Decision tree, as well as feature selection algorithms like Relief, MRMR, LASO, and Local learning to eliminate irrelevant and redundant features. We also suggested a fast conditional mutual information feature selection approach for feature selection. The features selection techniques improve classification accuracy and reduce classification system execution time. Additionally, leave one subject out cross-validation has been utilized to learn model evaluation best practices and hyper parameter tuning. Classifier performance is measured using metrics. Classifier performance was tested on features selected by features selection methods. Experimental results suggest that the proposed feature selection method (FCMIM) with classifier support vector machine can create a high-level intelligent heart disease detection system. More accurate than prior methods, the suggested diagnosis system (FCMIM-SVM) The proposed approach can also be readily deployed in healthcare to detect cardiac problems.

关键词： Heart Support vector machines Thallium Accuracy Pain Cardiac disease Feature extraction Classification algorithms Tuning Diseases

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Transforming Business Intelligence using Data Analytics and Artificial Intelligence 2

Transforming Business Intelligence using Data Analytics and ...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Jwala, V.Ch. Joshi, Bhavesh Mishra, Pavan Saha, Aahana Goyal, Shweta Ramesh, Bandla Srkr Engineering College Department of Information Technology Andhra Pradesh Bhimavaram India Graphic Era Hill University Scool of Management Bhimtal Campus India Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India Kr Mangalam University Department of Psychology Gurgaon India Graphic Era Deemed to be University Deptt. of Electrical Engineering Dehradun India St. Martin's Engineering College Department of Ai & Ml Dhullapally500100 India

ISBN: (纸本)9798350364699

A wide range of businesses values generation through decision-making is crucial use business intelligence solutions. Business Intelligence is the process of using existing data to extract, analyse, and forecast business-critical insights. This paper looks at how artificial intelligence and data analytics are revolutionising business intelligence and how this has drastically changed how businesses plan their plans and make decisions. Business need to use ml and business intelligence to obtain an competitive hand and pursue informed choices in the present information driven market. This study takes a gander at how strategies from machine learning can be applied to further develop significant business experiences. An apple dataset was exposed to three unique classifiers: Random Forest, Decision Tree, and SVM. This article shows how machine learning algorithms may revolutionise business intelligence and provide businesses with a competitive edge. Businesses may predict future trends, find hidden patterns, and automate decision-making processes by utilising machine learning algorithms. The importance of selecting the right classifier for predictive modelling is further shown by the fact that Random Forest outperformed all other classifiers evaluated by 92%. This study also emphasises how important it is for business organisations to use machine learning and data analytics in order to improve business insights and drive business success. © 2024 IEEE.

关键词： Adversarial machine learning

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Segmentation of Liver Tumors in CT Scans Using Active Contour Models and Machine Learning

Segmentation of Liver Tumors in CT Scans Using Active Contou...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Sharma, Rajita Alabdeli, Haider Baghel, Abhishek Singh Jayashankari, J. Maheswari, G. Dhanashree, S. Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Hindustan Institute of Technology and Science Department of Information Technology Chennai India Saveetha Institute of Medical and Technical Sciences Department of Ai & Ml Tamilnadu Chennai India

ISBN: (纸本)9798350390759

Liver tumor segmentation in CT scans plays a crucial role in cancer diagnosis and treatment planning. This paper proposes a novel methodology that integrates Active Contour Models (ACMs) with Convolutional Neural Networks (CNNs) for accurate and efficient segmentation of liver tumors. The approach leverages the feature learning capabilities of deep learning and the precise boundary detection of ACMs to achieve state-of-the-art segmentation results. Evaluation on the LiTS dataset demonstrates superior performance in terms of Dice Similarity Coefficient (DSC), Precision, Recall, and computation time. The proposed methodology offers a promising solution for improving clinical decision-making in liver cancer treatment. © 2024 IEEE.

关键词： Convolutional neural networks

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Attention Based Generative Adversarial Networks to Augment Image Data for Rapid Training

Attention Based Generative Adversarial Networks to Augment I...

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Vijayakumar Adaickalam Sadagopan Selvarasu Gowthul Alam M A Suresh Kumar S Annamalai Sangeetha N School of Computer Science and Engineering and Information Presidency University Bangalore Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai School of Computer Science and Engineering JAIN (Deemed-to-be University) Bangalore Department of AI & ML City Engineering College Bangalore

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

In this paper, we develop an Attention based Generative Adversarial Networks (AGAN) to augment image data for the purpose of robust training for efficient processing of the hyper spectral imaging. The AGAN model enables generation of images from the sample images that helps in training the classifier and in this study a fundamental classifier namely a convolutional neural network is used. A robust training is conducted to test the accuracy of detecting the instances effectively using the dataset. The simulation shows that the proposed AGAN-CNN attains improved accuracy after robust training than the existing methods.

关键词： Training Accuracy Training data Imaging Production computer architecture Generative adversarial networks Grid computing Convolutional neural networks Hyperspectral imaging

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Enhanced Movie Recommendation and Sentiment Analysis Model Achieved by Similarity Method through Cosine and Jaccard Similarity algorithms

Enhanced Movie Recommendation and Sentiment Analysis Model A...

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International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

作者： Ulligaddala Srinivasarao R Karthikeyan Prakash Kumar Sarangi Bhawani Sankar Panigrahi Department of Computer Science and Engineering (AI & ML) Vardhaman College of Engineering Hyderabad India

ISBN: (纸本)9781665462013

Nowadays, with technology at the fore front of almost every business, there is an abundance of data and information that is difficult to sort through and understand. Consequently, a recommendation system is beneficial in dealing with such a big amount of information and filtering out the valuable material that is rapid also, pertinent to the client’s choice. In order to suggest films that are similar to the one the user has chosen, this paper describes a method for a movie recommendation system that uses different similarity approaches. In order to improve the user experience, this system performs sentiment analyses the reviews of the movie that was selected using optimization techniques. To increase accuracy and efficiency, a novel recommends-based sentiment classification technique that combines the Support Vector Machine Classifier with the Harris Hawks optimization method is utilised. The proposed approach is capable of handling high-dimensional data, according to empirical findings on the dataset (Movie). In terms of classification accuracy, we found that the suggested method performs better than the other recommendation systems strategies examined in this study. The trial findings revealed that the suggested strategy had a success rate of 97% accuracy.

关键词： Support vector machines Sentiment analysis Analytical models Films Optimization methods Motion pictures User experience

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