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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： K.Jaya Deepthi T.Suresh Balakrishnan Prabhakar Krishnan U.Samson Ebenezar Nageshwari Department of AIML Mohan Babu University Tirupati-2 AP India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India IEEE Research Scientist Center for Cyber security Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri-campus India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering CARE College of Engineering Trichy Tamil Nadu

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

IoT and cloud technology are vital for large systems. An improved data storage technique for IoT applications and cloud distribution is described in this research. Scalability, reliability, latency, and resource usage are internal and external to the algorithm. The approach above scales well with work portion or dataset size. The first experiment shows the algorithm scaling perfectly even as IoT devices generate additional data. Experiment 2 proves linear scalability works for 100,000 IoT devices, while Experiment 3 efficiently organizes 1 million data points. It proved the algorithm by tolerating manufactured faults. To ensure data service availability, Experiment 1 tests the system’s operational dependability on node failures and network disturbances. With 0.1% downtime even under challenging conditions, experiment 2 showed the algorithm’s incredible robustness in real-world applications. To improve data organization, this experiment exhibits data consistency when nodes fail. Lower and constant time improves operation safety and reliability. Algorithm 1 shows how the algorithm can quickly answer data queries and keep up with the massive amount of data transfers, supporting low-latency IoT applications. Average data retrieval latency of experiment 2 is 10 ms, which is real-time for vital information. Trial 3 processes data online with a latency <20 ms, providing fast insights for speedy response and decision-making. The adaptive distributed workload uses little resources, making the approach efficient. Exp1 uses distributed nodes to share data and processing, improving task efficiency. In experiment 2, 60% of the CPU, 70% of the Memory tool, and 80% of the Storage system are used, demonstrating platform resource efficiency. Trial 3 provides optimal resource allocation with less than 5% waste, making it an effective system that passes all performance benchmarks.

关键词： Cloud computing Scalability Decision making Memory Distributed databases Robustness Real-time systems

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Development of Advanced Noise Filtering Techniques for Medical Image Enhancement

Development of Advanced Noise Filtering Techniques for Medic...

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Intelligent Communication Technologies and Virtual Mobile networks (ICICV), International Conference on

作者： Sumit Kushwaha K. Amuthachenthiru Geetha. K Jonnadula Narasimharao Dileep Kumar M Sai Sudha Gadde Department of Computer Applications University Institute of Computing Chandigarh University Punjab India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Faculty of Management Sciences Hensard University Bayelsa Nigeria Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Telangana India

Medical imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), X-rays, and ultrasound, are extensively employed in the healthcare industry for diagnostic purposes. Noise, on the other hand, can disturb these approaches and lead to inaccurate diagnosis. Due to this problem, the significance of noise removal technologies has grown in the field of medical imaging, specifically for the examination and understanding of medical images and anatomical structures. To tackle these challenges, a variety of denoising techniques have been developed, such as the Weiner filter (WF), Gaussian filter, and median filter. This work employs a Deep Learning (DL) method known as Convolutional Autoencoder (CAE) to remove noise from medical images. We gather chest X-ray images for analysis and focus on common noise types that affect medical images, such as Poisson and Gaussian noise. We evaluate the effectiveness of the suggested denoising method CAE by comparing it to the commonly employed WF methodology. The Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) are employed to assess and compare the performance of the WF and CAE in reducing Poisson and Gaussian noise. The study's findings indicate that the suggested denoising method based on CAE shows promising results in terms of improving quality.

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Exhaled Breath Analysis Through the Lens of Molecular Communication: A Survey

arXiv

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arXiv 2025年

作者： Bhattacharjee, Sunasheer Bi, Dadi Hofmann, Pit Wietfeld, Alexander Becke, Sophie Lommel, Michael Zhou, Pengjie Zheng, Ruifeng Kertzscher, Ulrich Deng, Yansha Kellerer, Wolfgang Fitzek, Frank H.P. Dressler, Falko School of Electrical Engineering and Computer Science TU Berlin Germany Department of Engineering King’s College London United Kingdom Deutsche Telekom Chair of Communication Networks Technische Universität Dresden Germany Dresden Germany Chair of Communication Networks Technical University of Munich Germany Institute of Computer-Assisted Cardiovascular Medicine Deutsches Herzzentrum der Charité Charité - Universitätsmedizin Berlin Germany

Molecular Communication (MC) has long been envisioned to enable an Internet of Bio-Nano Things (IoBNT) with medical applications, where nanomachines within the human body conduct monitoring, diagnosis, and therapy at micro- and nanoscale levels. MC involves information transfer via molecules and is supported by well-established theoretical models. However, practically achieving reliable, energy-efficient, and bio-compatible communication at these scales still remains a challenge. Air-Based Molecular Communication (ABMC) is a type of MC that operates over larger, meter-scale distances and extends even outside the human body. Therefore, devices and techniques to realize ABMC are readily accessible, and associated use cases can be very promising in the near future. Exhaled breath analysis has previously been proposed. It provides a noninvasive approach for health monitoring, leveraging existing commercial sensor technologies and reducing deployment barriers. The breath contains a diverse range of molecules and particles that serve as biomarkers linked to various physiological and pathological conditions. The plethora of proven methods, models, and optimization approaches in MC enable macroscale breath analysis, treating human as the transmitter, the breath as the information carrier, and macroscale sensors as the receiver. Using ABMC to interface with the inherent dynamic networks of cells, tissues, and organs could create a novel Internet of Bio Things (IoBT), a preliminary macroscale stage of the IoBNT. This survey extensively reviews exhaled breath modeling and analysis through the lens of MC, offering insights into theoretical frameworks and practical implementations from ABMC, bringing the IoBT a step closer to real-world use. Copyright © 2025, The Authors. All rights reserved.

关键词： Medical nanotechnology

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Implementation of Indexing Techniques to Prevent data Leakage and Duplication in Internet

Implementation of Indexing Techniques to Prevent Data Leakag...

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2022 IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2022

作者： Nalini, M.K. Dhinakaran, K. Elantamilan, D. Gnanavel, R. Vinod, D. BMS College of Engineering Department of ISE Bangalore India Dhanalakshmi College of Engineering Department of Artificial Intelligence and Data Science Chennai India VallalP. T. LeeChengalvarayaNaicker Arts and Science College Department of Computer Science Chennai India R.M.K. College of Engineering and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Dept of CSE Chennai India

ISBN: (纸本)9781665495295

Research in this area aims to create a new and efficient method for detecting near-duplicates in online content. Web pages that a search engine has scoured are first parsed to remove HTML elements and java scripts. After this phase, remove common keywords or stop words from the crawled pages. The affixes (prefix and suffix) from crawled pages are drawn by extracting keywords from the text. Finally, the collected keywords generate the similarity score between two papers. This research has proved that the suggested algorithms outperform earlier ones by conducting an extensive experimental investigation on several actual datasets. New algorithms outperform those already in use, according to the test findings. Since there are so many documents on the internet, web search engines have had difficulty finding relevant results for their customers. Overhead costs for search engines have risen dramatically due to an overabundance of almost similar or identical documents somehow. The web crawling community has long been aware of duplicate and near-identical online pages. Users want search engines to return relevant results for their searches on the first page, free of duplicates and redundancies. This is an important criterion. An essential enabler in the health industry is record or data linking, as connected data is an economic resource that may aid enhance research into health policy, detecting bad medication responses, and cutting expenses while uncovering health system fraud. Recent years have seen significant progress in several record linking approaches, mostly due to breakthroughs in data mining and machine learning. Most of these novel technologies have not yet been incorporated in current record linking systems or are disguised in commercial software as 'black boxes.' When it comes to learning about new record linking strategies and comparing old methods with new ones, this makes it difficult for users to do so. Innovative record-linking systems must be tested and im

关键词： Search engines

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An Advanced and Ideal Method for Tumor Detection and Classification from MRI Image Using Gaussian Mixture and RCNN

An Advanced and Ideal Method for Tumor Detection and Classif...

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

作者： Samrat Ray K. Ramkumar Ch. V Anupama P. Misba Marybai K Anguraju P. Preethi Dean and Head of International Relations IIMS Pune India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamilnadu Department: Information Technology Malla Reddy Engineering College Hyderabad Telengana Artificial Intelligence and Data Science department Panimalar Engineering College Chennai Department of Computer Science and Engineering Kongunadu College of Engineering and Technology Trichy Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

Extensive research and methodological improvements over time attest to the necessity of brain tumour detection and analysis for any indication system. Accurate tumour detection is essential for this study; hence an efficient automated method must be implemented Numerous segmentation algorithms have been developed to classify brain tumors with greater precision. Medical image processing experts agree that brain picture segmentation presents unique difficulties. A novel automatic detection and classification system is proposed in this work. The suggested method is broken down into several stages, including preprocessing of MRI images, segmentation of those images, extraction of features, and classification of those features. The noise in the MRI picture is reduced during the preprocessing stage by applying an adaptive filter. The grey level co-occurrence matrix (GLCM) is used for feature extraction, while the improved Means clustering (IMC) technique is used for image segmentation. To categorize MRI scans, we used a deep learning algorithm scan after features were extracted from the scans them as benign lesions, gliomas, meningiomas, or pituitary tumours. In order to accomplish the classification task, recurrent convolutional neural networks (RCNN) were utilised. For a given input dataset containing photographs of the brain, the proposed technique yields superior classification results. The investigations made use of MRI pictures from Kaggle's data sets, which included 464 testing sets and 3264 training sets. The outcomes show that the proposed strategy outperforms its predecessors. The suggested RCNN method is then compared to BP, VG-Net, and RCNN, three of the most popular classification techniques currently in use. The suggested classifier successfully identified 96.21 percent of brain tumour tissues in magnetic resonance imaging scans.

关键词： Deep learning Training Image segmentation Magnetic resonance imaging Noise Feature extraction Classification algorithms Convolutional neural networks Tumors Testing

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Convolutional Neural Network-based image tamper detection with Error Level Analysis

Convolutional Neural Network-based image tamper detection wi...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Manjunatha S Swetha M D Rashmi S Ananda Kumar Subramanian Vinoth Kumar V Mallikarjuna Swamy S Department of Information Science and Engineering Global Academy of Technology Bengaluru India Department of Computer Science and Engineering BNM Institute of Technology Bengaluru India Department of Computer Science and Engineering (Data Science) Dayananda Sagar College of Engineering Bengaluru India Dept of Computational Intelligence School of Computer Science Engineering VIT – University Vellore India School of Computer Science Engineering and Information Systems VIT – University Vellore India Dept of Electronics and Communication Engineering JSS Academy of Technical Education Bengaluru India

Photography is the most important, powerful, and reliable means of expression. Today, digital images not only provide disinformation but also act as agents for secret communication. Users and editing professionals work with digital images for a variety of purposes. Images are often regarded as facts or proof of reality, so they are misleading and fake news or publications of any form that use images manipulated in a highly misleading way. To recognize image tampering needs multiple image data and a model that can handle all the pixels in the image. Furthermore, training the data more efficiently and needed flexibility support everyday use. Models based on Deep learning such as Convolutional Neural networks with error level analysis (ELA) are the perfect solution.

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Machine Learning-Enhanced Analysis of Genomic data for Precision Medicine

Machine Learning-Enhanced Analysis of Genomic Data for Preci...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： R. Sasikala K. Jaya Deepthi T. Suresh Balakrishnan Prabhakar Krishnan U. Samson Ebenezar Department Of Computer Science And Engineering Care College Of Engineering Trichy Tamil Nadu Department Of Aiml Mohan Babu University Tirupati-2 Ap India Department Of Computer Science And Engineering Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Chennai India Center For Cyber Security Systems And Networks Amrita Vishwa Vidyapeetham Amritapuri-Campus India Department Of Computer Science And Engineering Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

Precision medicine employs genetic data, which is enhanced with machine learning algorithms, to determine the most relevant treatment method per patient’s genotype. We have zoned in on the area of a machine learning-guided genetic information analysis in this study to assist in clinical decisionmaking of precision medicine while improvising. Genetic information for 500 cancer patients cases taken from the public database and cleaned to lean out the desired features were furnished. Different machine learning algorithms were trained, including SVM, random forest, gradient boost, deep learning (CNN) and ensemble (ensemble method). Their performance was investigated for predicting cancer subtypes. The ensemble model reached the top accuracy level ($88 \%$), the best sensitivity ($85 \%$), ever specificity ($91 \%$) and AUC score (0.95). By comparison a group had better result than an individual algorithm could across all metrics. In further, deep learning methods were reported to attain more accuracy and F1 scores which is more than traditional machine learning methods. These findings hence prequalify the usefulness of ML in investigating genotype data in precision medicine, and enable physicians with useful information to select treatment options, make prognosis, and indicate the medical care.

关键词： Deep learning Support vector machines Machine learning algorithms Accuracy Sensitivity Precision medicine Genomics

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Dynamic Topology Management in Ad-Hoc networks for Improved Performance

Dynamic Topology Management in Ad-Hoc Networks for Improved ...

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International Conference on Advances in Information Technology (ICAIT)

作者： A.S. Anakath R. Kannadasan G. Simi Margarat A. Pasumpon Pandian Antony Sibiya Varghese V Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Vellore Tamil Nadu India Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering CARE College of Engineering Trichy India Department of Artificial Intelligence and Data Science GRT Institute of Engineering and Technology Tiruttani Tamil Nadu India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Optimization in ad hoc networks is a highly specialized task because the structure of the network is loosely formed, and each node is independently responsible for its operation. In order to obtain a better result from this solely formed networks this report offer extensive Dynamic Topology Management (DTM) method. Dynamic routing protocols lager lines, dividend balancing, Life-saving administration, guarantee of reliability, cross-layer design, security aspects and the on-time monitoring and discovery of topology changes are the main objectives and steps to be accomplished. Real-time Location monitoring and forest structure discovery are made possible by employing adaptive routing protocols, namely Dynamic Source Routing(DSR) and Ad-Hoc On-Demand Distance Vector(AODV). Also, the algorithms are capable of making advance adjustments to routing routes and resource allocations drawn from the mobility patterns forecasting which is vital. Load balancing methods, which distribute traffic in a network evenly, enable the system to run efficiently and avoid problems caused by overloading. Algorithms that are energy-efficient and make the best use of energy resources having their topology's optimized are introduced to the streamlined energy distribution systems. These algorithms are focused on the node's energy level. Through fault tolerance techniques which recognize and handle failures in nodes and partitioned networks, connectedness activity is a continuous one. Through QoS perception, the network topology might automatically be tailored to conform to the given quality criteria, thus not restricting the choice for differing applications. The proposed could have defenses against adversary nodes and their potential DDoS attacks which will enhance network protection. Yet another advantage of an architectural style that promotes cross-layer collaboration is enhanced topology management which is the goal around which the functions of various levels of the protocol stack revolve

关键词： Network topology Aerodynamics Routing Ad hoc networks Routing protocols Real-time systems Energy efficiency

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Optimizing Healthcare Delivery through CloudBased Clinical Decision Support Systems

Optimizing Healthcare Delivery through CloudBased Clinical D...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： U. Samson Ebenezar Jenila G. Vennila T. Suresh Balakrishnan Prabhakar Krishnan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering CARE College of Engineering Trichy Tamil Nadu Department of AI & ML School of Computing Mohan Babu University Tirupati Andhra Pradesh Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Center for Cyber security Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri-campus India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

The experiment aims at evaluating how four machine learning algorithms work: Decision Tree, Random Forest, SVM, and Neural Network, in a task, of binary classification. Models were evaluated using comprehensive metrics such as accuracy, precision, recall, F-score and AUC that revealed a good diagnostic performance. Models selected for the experiment and parameters tested on real datasets show steady changes in performance. The Neural Network model remained on top, achieving accuracy, precision, recall, F-score and AUC-ROC of 91.8%, 92.1%, 91.3%, 91.7% and 0.93 respectively. Random Forest displayed a competitive accordance along with the accuracy of 88.6%, precision of 89.2%, recall of 88.7%, and the F-score of 89.0%, and the AUC of 0.91. From the results, Support Vector Machine and Decision Tree models posted lower accuracy, recall, and fscores compared to the Neural Network and Random Forest models in most cases. These reveal the need for the improvement of the suitable machine learning model that comes from specific task needs as well as the consideration of each one of factors including accuracy, interpretability, and computational efficiency. Extensive and development of the model may result in more comprehensive understanding of how the model leads classifying of real-world applications such as medical diagnoses, fraud detection and target marketing.

关键词： Support vector machines Measurement Accuracy Machine learning algorithms Computational modeling Neural networks Medical services

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Error bounds for kernel-based approximations of the Koopman operator

arXiv

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arXiv 2023年

作者： Philipp, Friedrich Schaller, Manuel Worthmann, Karl Peitz, Sebastian Nüske, Feliks Technische Universität Ilmenau Institute for Mathematics Weimarer Strasse 25 IlmenauD-98693 Germany Paderborn University Department of Computer Science Data Science for Engineering Germany Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany

We consider the data-driven approximation of the Koopman operator for stochastic differential equations on reproducing kernel Hilbert spaces (RKHS). Our focus is on the estimation error if the data are collected from long-term ergodic simulations. We derive both an exact expression for the variance of the kernel cross-covariance operator, measured in the Hilbert-Schmidt norm, and probabilistic bounds for the finite-data estimation error. Moreover, we derive a bound on the prediction error of observables in the RKHS using a finite Mercer series expansion. Further, assuming Koopman-invariance of the RKHS, we provide bounds on the full approximation error. Numerical experiments using the Ornstein-Uhlenbeck process illustrate our *** Codes 47D07, 47B32, 47B33, 62M15 © 2023, CC BY.

关键词： Stochastic systems

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