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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Amandeep Nagpal S Vinod Kumar Uma Reddy K. Radha Ashwani Kumar Namaat R. Abdulla Lovely Professional University Phagwara Institute of Aeronautical Engineering Dundigal Hyderabad Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore AP Dept. of IT St. Martin’s Engineering College Secunderabad Telangana India Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh College of Health and Medical Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

Because cybersecurity is continually changing, it’s crucial to have effective, adaptable defenses. This paper demonstrates a novel computer security method. This is done via blockchain technology, enhanced cryptography, threat intelligence integration, machine learning-based strangeness detection, and behavioral analysis. After carefully comparing our solution to others, we feel it is the best way to secure computer and network systems. The Machine Learning-Based Anomaly Identification approach accurately identifies anomalies using complex mathematical calculations and repetitive procedures to adjust parameters. Blockchain technology unites odd data into a decentralized ledger using encryption for forensics efficiency. Homomorphic encryption and quantum-resistant signatures protect the program against new attacks. The proposed method exhibits superior performance across various metrics, achieving a detection accuracy of 98.5% with a low false positive rate of 1.0%. Visualizations elucidate trade-offs, trends, and classification metrics. This cybersecurity framework offers a promising solution for addressing the dynamic challenges posed by a wide range of cybersecurity threats. Its adaptability, versatility, and collective efficacy position it as a noteworthy advancement in the pursuit of securing digital environments from evolving and sophisticated cyber threats.

关键词： Measurement Technological innovation Accuracy Forensics Current measurement Market research Blockchains Computer security Usability Network systems

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EEG based BCI for Autonomous Control: A Review

EEG based BCI for Autonomous Control: A Review

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IEEE Southeastcon

作者： Sanad Biswas W David Hairston Jason S. Metcalfe Sylvia Bhattacharya Dept. of Analytics and Data Science Kennesaw State University Marietta GA U.S US DEVCOM Army Research Laboratory Aberdeen Proving Ground MD U.S MD U.S Dept. of Engineering Technology Kennesaw State University Marietta GA U.S

Brain-computer interface (BCI) has had exciting improvements in the last decade due to the developments in brain science and computer technology, making BCI a top research area in applied science. With the advancement of technologies, researchers are now experimenting using BCI for operating vehicles, robots, and aircraft. However, choosing the suitable brain signal acquisition technique among the available ones is a big concern. In this paper, we will survey the studies that used the BCI framework for controlling vehicles and quadcopters, its limitations, its prospect, and the reason electroencephalogram (EEG) best suits the BCI framework.

关键词： Surveys Visualization Prototypes Gaze tracking Electroencephalography Brain-computer interfaces Reliability

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XANNLight: A Novel Hybrid Ensemble Model for Accurate Health Insurance Prediction and Trend Forecasting

XANNLight: A Novel Hybrid Ensemble Model for Accurate Health...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Kafila Sajja Suneel Jayendra Gopal Thatipudi M Jini Kumari K. Penyameen Anubhav Sharma School of Business SR University Warangal Telangana India Department of CSE(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Dept. of Computer Science Jayendra Gopal Thatipudi Student University of North Texas USA Department of ECE Chennai Institute of Technology Velammal College Of Engineering And Technology Madurai Department of CSE IES institute of Technology and management IES University Bhopal Mp India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Health insurance is a critical industry that requires precise data analysis and predictive capabilities to ensure operational efficiency and cost management. In an era where large datasets are readily available, machine learning models have become indispensable for predicting claims and identifying trends in insurance data. The rapid growth of the health insurance industry has increased the need for accurate prediction models to optimize claim management and trend forecasting. This study presents XANNLight, a novel hybrid ensemble model that integrates Artificial Neural Networks (ANN) and LightGBM to predict health insurance claims with high precision. The proposed model handles complex, non-linear relationships using ANN while LightGBM provides fast and accurate predictions for structured data. The dataset used for model training and evaluation consists of 1338 records, with features including age, sex, BMI, number of children, smoker status, and region. The model's performance is evaluated based on accuracy, precision, recall, and F1 score. The XANNLight model achieves an accuracy of 98.12%, outperforming nine existing models, including Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting, which achieved accuracy levels between 85% and 96%. Furthermore, XANNLight demonstrates superior precision (97.83%), recall (97.90 % ), and F1 score (97.86 % ). These results highlight the model's efficiency in processing large datasets and predicting insurance claims, making it an effective tool for health insurance companies aiming to enhance operational efficiency and cost management.

关键词： Industries Accuracy Costs Computational modeling Insurance Artificial neural networks Predictive models Market research data models Forecasting

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An Improved Method for Fault Detection Electric Power System Protection using SVMCNN Model

An Improved Method for Fault Detection Electric Power System...

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Electronics, Computing, Communication and Control Technology (ICECCC), International Conference on

作者： NMG Kumar S.Shanmuga Priya Vineetha Varghese Renuga K Jyoti Prasad Patra Neerav Nishant Dept. of EEE Mohan Babu University Sree Vidyanikethan Engineering College Rangam Pet Tirupathi India Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai Tamilnadu India Department of Computer Science and Engineering Rathinam Technical campus Coimbatore Tamilnadu India Department of Mathematics St. Joseph's Institute of Technology OMR Chennai Tamilnadu India EE And EEE Krupajal Engineering College-KEC Puri Odisha India Department of Computer Science and Engineering School of Engineering Babu Banarasi Das University Lucknow India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371819

Multiple factors outside our control can contribute to the unanticipated breakdown of electrical power networks. It is of the utmost importance to prevent unforeseen power system issues from affecting other components of the protective system. It is the job of the protective systems to identify and categorize potentially dangerous voltage and/or current lines. To isolate the malfunctioning line, utilize the protection relay to trigger the circuit breaker. An SVM-CNN architecture is presented in this study for the purpose of detecting power outages in transmission lines. When it comes to preprocessing, feature selection, and training models, the correct sequence of activities is crucial. For anomaly-based fault detection algorithms to accurately represent and understand the data, data preprocessing is essential. Feature selection makes the process more feasible by reducing the entire data volume and helping HIF detection home in on the most relevant features. The results were evident with an accuracy improvement reaching 95.70%.

关键词： Training Support vector machines Power transmission lines Fault detection Computational modeling data preprocessing Feature extraction

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Experimental Analysis of Radioscopy Images based Pneumonia Disease Detection using Improved Neural Network Methodology

Experimental Analysis of Radioscopy Images based Pneumonia D...

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Image Processing and Capsule Networks (ICIPCN), International Conference on

作者： Sajja Suneel B.M.S. Rani M. Amina Begum Jayendra Gopal Thatipudi Ajay Singh Yadav D. Muthukumaran Department of CSE(Data Science) Institute of Aeronautical Engineering Hyderabad Telangana India Department of ECE Vignan’s Nirula Institute of technology and science for women Department of ECE Oxford College of Engineering Venmani Tamilnadu India Dept. of Computer Science University of North Texas Department of Mathematics SRM Institute of Science and Technology Delhi-NCR Campus Ghaziabad U.P India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

The bacterium Streptococcus pneumonia is the most prevalent causative agent of pneumonia, a severe and potentially fatal lung infection in humans. According to the World Health Organization, pneumonia accounts for one in three deaths in India. Assessing chest radiographs to detect pneumonia typically requires skilled radiologists, making an automatic detection method crucial for rapid treatment, especially in rural areas. This study introduces an Improved Neural Network (INN) learning method, cross-validated against existing Convolutional Neural Networks (CNNs), reflecting the success of deep-learning algorithms in medical image analysis. The research highlights the utility of these methods for disease classification. The features learned by INN and CNN models on large datasets are particularly valuable for image classification tasks. By using feature extractors programmed on trained INN and CNN frameworks, this study compares the performance of various classifiers in identifying normal and abnormal chest radiographs. Statistical results indicate that pre-trained INN models combined with supervised classifier approaches significantly enhance the evaluation of chest radiographs, particularly in detecting pneumonia. This advancement is pivotal for improving diagnosis and treatment outcomes in resource-limited environment.

关键词： Learning systems Pneumonia Neural networks Lung Organizations Feature extraction Convolutional neural networks

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Accurate Brain Disease Diagnosis with UE-CNN Uncertainty Discrimination

Accurate Brain Disease Diagnosis with UE-CNN Uncertainty Dis...

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

作者： S. Hrushikesava Raju S. Adinarayna N. Merrin Prasanna Shaik Jumlesha U. Sesadri V. Nandini Department of CSE KoneruLakshmaiah Education Foundation Green Fields Vaddeswaram Guntur Dept of CSE(Data Science) Raghu Engineering College Visakhapatnam Dept. of ECE Annamacharya Institute Of Technology & Sciences(Autonomous) Rajampet Andhra Pradesh Department of CSE Annamacharya Institute of Technology and Science Tirupati Chittoor(dt) AP Department of CSE Vardhaman College of Engineering Hyderabad Telangana

Detecting uncertainty in brain disease diagnosis is crucial to avoid wrong interpretations and improve patient health. This research work proposes a novel technique, UE-CNN (Uncertainty Estimation Convolutional Neural Network) to measure the uncertainty in the process of brain disease diagnosis. The CNN layers classify uncertainty while processing the input data. Here, various methodologies and domains are employed to detect uncertainty. By removing uncertainty from the input brain images, the system's efficiency can be enhanced. The key performance measures in this study are accuracy and efficiency, wherein the detection of brain diseases is the ultimate goal. Only the significant diseases impacting a large population are considered from the dataset. The proposed UE-CNN technique performs uncertainty detection and achieves the goal of accurate brain disease detection and classification. From the results, it is evident that the proposed UE-CNN outperforms the other existing CNN models in terms of accuracy and overall performance.

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Detection of Pre Shoplifting Suspicious Behavior Using Deep Learning

Detection of Pre Shoplifting Suspicious Behavior Using Deep ...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Sujan Shrestha Yoji Taniguchi Tetsuo Tanaka Course of Information and computer sciences Graduate school of Engineering Kanagawa Institute of Tehnology Atsugi Japan Department of Information Artificial Intelligence and Data Science Daiichi Institute of Technology Tokyo Japan Dept. of Information and Computer Sciences Faculty of Information Technology Kanagawa Institute of Technology Atsugi Japan

ISBN: (数字)9798350377903

ISBN: (纸本)9798350377910

Shoplifting presents a significant challenge for retailers, leading to substantial financial losses and security concerns. Current surveillance systems effectively identify shoplifting incidents postoccurrence; however, the capability to detect and prevent such activities preemptively remains limited. This research focuses on identifying behaviors that likely precede shoplifting events. To this end, we introduce a novel benchmark dataset, specifically designed for the early detection of suspicious behavior that may lead to shoplifting. This dataset is curated by selecting and segmenting relevant sequences from the UCF101-Crime dataset, encompassing suspicious behavior, actual shoplifting incidents, and normal activities. Our approach addresses the limitations of existing datasets where the brief duration of shoplifting events may skew neural network performance. The dataset includes clips of only a few seconds each, for which we propose a robust neural network architecture combining the strengths of convolutional neural networks (CNN) for efficient feature extraction and Bidirectional long short-term memory (BiLSTM) networks for capturing temporal dependencies within action sequences. Our model achieves an impressive classification accuracy of 86.21 % on the newly created dataset.

关键词： Deep learning Recurrent neural networks Accuracy Surveillance Benchmark testing Feature extraction Convolutional neural networks Security Informatics Long short term memory

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Õ(n+poly(k))-time Algorithm for Bounded Tree Edit Distance

Õ(n+poly(k))-time Algorithm for Bounded Tree Edit Distance

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Annual IEEE Symposium on Foundations of Computer science

作者： Debarati Das Jacob Gilbert MohammadTaghi Hajiaghayi Tomasz Kociumaka Barna Saha Hamed Saleh Dept. of Computer Science & Engineering Pennsylvania State University State College PA United States Dept. of Computer Science University of Maryland College Park MD United States Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany Dept. of Computer Science & Engineering Halıcıoğlu Data Science Institute University of California San Diego San Diego CA United States

ISBN: (数字)9781665455190

ISBN: (纸本)9781665455206

Computing the edit distance of two strings is one of the most basic problems in computer science and combinatorial optimization. Tree edit distance is a natural generalization of edit distance in which the task is to compute a measure of dissimilarity between two (unweighted) rooted trees with node labels. Perhaps the most notable recent application of tree edit distance is in NoSQL big databases, such as MongoDB, where each row of the database is a JSON document represented as a labeled rooted tree and finding dissimilarity between two rows is a basic operation. Until recently, the fastest algorithm for tree edit distance ran in cubic time (Demaine, Mozes, Rossman, Weimann; TALG’10); however, Mao (FOCS’21) broke the cubic barrier for the tree edit distance problem using fast matrix *** a parameter k as an upper bound on the distance, an $\mathcal{O}(n+k^{2})$-time algorithm for edit distance has been known since the 1980s due to works of Myers (Algorithmica’86) and Landau and Vishkin (JCSS’88). The existence of an $\tilde{\mathcal{O}}(n+poly(k))$-time algorithm for tree edit distance has been posed as open question, e.g., by Akmal and Jin (ICALP’21), who give a stateof-the-art $O(nk^{2})$-time algorithm. In this paper, we answer this question positively.

关键词： Computer science Upper bound Costs databases Task analysis Optimization

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iCardo 2.0: A Smart Healthcare Framework for Cardiovascular Disease Accurate Prediction by Using T-Wave Morphology of ECG

iCardo 2.0: A Smart Healthcare Framework for Cardiovascular ...

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IEEE International Symposium on Smart Electronic Systems (iSES)

作者： Nidhi Sinha Amit M. Joshi Saraju P. Mohanty Department of ECE MNIT Jaipur India Department of Data Science and Engineering Konverge.AI Pune India Dept. of CSE University of North Texas Denton USA

ISBN: (纸本)9798350399233

Potassium plays a vital role in the heart's normal functioning. Potassium homeostasis disorder can elevate the risk of heart failure, and acute treatment may not be tolerated in the long term. Hyperkalemia and cardiac failure are linked through a vicious cycle. In this study, the association of blood potassium level to heart failure has been scrutinized with the help of the BIDMC Congestive Heart Failure dataset. The dataset contains ECG recordings of 15 subjects of NYHA Class III-IV patients. Eleven of them were men between the ages of 22 and 71, while four were women between the ages of 54 and 63. T wave morphology of ECG recording can quantify the information of blood potassium level, and this concept is already well-validated. Here, in this work threshold for the amplitude of T was set to 10 mm of height, and the concept has been authenticated using the MIT-BIH Normal Sinus Rhythm database. Based on the T wave morphology, 12 out of 15 subjects of the BIDMS dataset were found to be hyperkalemic. There were two subjects which were hyperkalemic, whereas one subject did not exhibit a distinguishable T wave. There are around 90% f heart failure patients in the database are hyperkalemic, indicating a strong linkage of heart disease with potassium level. The proposed smart healthcare system would be integrated for hyperkalemia prediction through remote monitoring. The framework can be utilised to forecast the occurrence of hyperkalemia for CVD patients using recorded ECG.

关键词： Heart databases Biological system modeling Morphology Electrocardiography Recording Congestive heart failure

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Z-Number-Valued Rule-Based Classification System

SSRN

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SSRN 2022年

作者： Li, Yangxue Herrera-Viedma, Enrique Pérez, Ignacio Javier Barragán-Guzmán, Mónica Morente-Molinera, Juan Antonio Andalusian Research Institute in Data Science and Computational Intelligence Dept. of Computer Science and AI University of Granada Granada18071 Spain Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

The fuzzy rule-based classification system (FRBCS) is a popular tool in classification problems due to its interpretability and comprehensibility. As an extension of fuzzy numbers, the concept of Z-number is a more appropriate formal structure to describe uncertain and partially reliable information. A Z-number is an ordered pair of fuzzy numbers, where the second fuzzy number describes the reliability of the first one. As a result of its representation capability, it can receive better classification results. However, there is still a gap in the application of Z-numbers to classification problems due to its high computation complexity. To take advantage of the Z-number, we design a simple way to make Z-numbers apply to classification problems. Using the second fuzzy number to adjust the first fuzzy number to fit the unevenly distributed data. Then we create a Z-number-valued if-then rule by extending the fuzzy if-then rule. In addition, a Z-number-valued rule-based classification system (ZRBCS) is developed, including two main processes: rules generation and new pattern classification. Compared to the classic fuzzy rule-based system, the proposed system can cover more information that can improve classification results. We evaluated the proposed method on 20 datasets to compare its learning and predictive abilities against the classic fuzzy rule-based system. The results demonstrate the Z-number-valued rule-based classification system is superior to the fuzzy rule-based classification system. © 2022, The Authors. All rights reserved.

关键词： Classification (of information)

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