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4th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2021

作者： Bhat, Mohammad Idrees Sharada, B. Sinha, Manish Kumar University Institute of Computing Chandigarh University Mohali140413 India Department of Studies in Computer Science University of Mysore Mysore India SVIT Secunderabad India

ISBN: (纸本)9783031070044

In this paper, we make an attempt to give graph representation to handwritten Devanagari words. Each edge in resulted word graph is weighted with its corresponding stretching (or length). For transforming/embedding word graphs into feature vectors their Eigen decomposition aka spectral decomposition is carried out from their weighted associated matrices. Since, these associated matrices contain complementary information we fused their sorted Eigen values at decision level by exploiting multi-class support vector machines (SVM). In order to corroborate the experimental results, we employed Legal amount dataset. From the experimentation, we observe some interesting insights that might be useful for future investigations. © 2022, Springer Nature Switzerland AG.

关键词： Graphic methods

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Driver Health Monitoring System to Identify Cardiac Problems in Drivers using Live ECG Signal

Driver Health Monitoring System to Identify Cardiac Problems...

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： M. K. Arif Kalaivani Kathirvelu A. Yasir Department of Computer Science and Engineering School of Engineering Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

According to contemporary estimations, a significant proportion of elderly drivers are prone to experiencing accidents or collisions due to their diverse health states during the act of driving. Currently, individuals in the middle-age and younger demographics are encountering comparable challenges. Research indicates that drivers who experienced crashes caused by medical emergencies are not influenced by vehicle design or the integrity of the road, as seen by the sort of crashes and the manner in which they occurred. This research presents a comparison and measurement of heart rate and heart variability data for the occurrence of heart disease, stroke, or any emergency situation. The methods employed include the use of electrocardiogram (ECG) and Photo Plethysmo Graphy (PPG) *** enhanced performance of deep learning over conventional machine learning methods in addressing various artificial intelligence tasks, including image processing, computer vision, object detection, speech recognition, and medical imaging, has propelled deep learning to become the dominant term in the field of Artificial Intelligence applications

关键词： Deep learning Wrist Technological innovation Speech recognition Electrocardiography Task analysis Monitoring

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On the Cycle Structure of Some Nonlinear Feedback Shift Registers

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Chinese Journal of Electronics 2025年第4期23卷 801-804页

作者： Zhongxiao Wang Hong Xu Wenfeng Qi State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Information Science and Technology Institute Zhengzhou China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

In this paper, we partly determine the cycle structure of two types of Nonlinear feedback shift registers (NFSRs). Based on these results, the cycle structure of a class of NFSRs with symmetric feedback functions can be completely characterized. Furthermore, an alternative proof of Kjeldsen's results is presented. Compared with the original proof based on abstract algebra theory, ours is straightforward and easy to understand.

关键词： Abstract algebra Shift registers Cryptography

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Low-depth quantum symmetrization

arXiv

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arXiv 2024年

作者： Liu, Zhenning Childs, Andrew M. Gottesman, Daniel Department of Computer Science Institute for Advanced Computer Studies Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States

Quantum symmetrization is the task of transforming a non-strictly increasing list of n integers into an equal superposition of all permutations of the list (or more generally, performing this operation coherently on a superposition of such lists). This task plays a key role in initial state preparation for first-quantized simulations. A weaker version of symmetrization in which the input list is strictly increasing has been extensively studied due to its application to fermionic systems, but the general symmetrization problem with repetitions in the input list has not been solved previously. We present the first quantum algorithm for the general symmetrization problem. Our algorithm, based on (quantum) sorting networks, uses O(log n) depth and O(nlog m) ancilla qubits where m is the greatest possible value of the list, enabling efficient simulation of bosonic quantum systems in first quantization. Furthermore, our algorithm can prepare (superpositions of) Dicke states of any Hamming weight in O(log n) depth using O(nlog n) ancilla qubits. We also propose an O(log2 n)depth quantum algorithm to transform second-quantized states to first-quantized states. Using this algorithm, QFT-based quantum telescope arrays can image brighter photon sources, extending quantum interferometric imaging systems to a new regime. Copyright © 2024, The Authors. All rights reserved.

关键词： Qubits

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Optimization Model for Machine Learning Based Intrusion Detection System (OML-IDS) in Mobile-Edge computing 4

Optimization Model for Machine Learning Based Intrusion Dete...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Kiruthiga, R. Jeyalaksshmi, S. Vanitha, V. Khan, Abdul Azeem Pal, Souvik Kumar, Rahul SRM Institute of Science & Technology Department of Computer Applications Kattankulathur Chennai India Depart. of IT Vels Institute of Science Technology & Advanced Studies Chennai India Sri Sankara Arts and Science College Department of Computer Applications Kanchipuram India Faculty of Islamic Technology Brunei Sister Nivedita University Department of CS and Engineering Kolkata India Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

The Internet of Things (IoT) and 5G communications now primarily use mobile edge computing (MEC). The fundamental concept behind MEC is the deployment of a hierarchy of edge servers with more computational power to handle the computing duties that are being offloaded by low-end IoT edge devices. Some of the security risks in MEC pertain to the application layer, the network layer, the data, and the node. Out main aim is to protect MEC from different cyber-attacks and continuous monitoring of digital assets. The network layer security for MEC is protected by intrusion detection systems (IDSs). We need an updated IDS system capable of detecting modern cyber-attacks as well as monitoring the devices. Thus, this paper proposes an optimization model for MEC settings that functions in conjunction with an OML-IDS, a machine learning-based intrusion detection system. To begin identifying attack patterns in a MEC environment, this model first develops an optimization-based feature selection strategy for selecting an ideal subset of characteristics. For the purpose of intrusion categorization and detection, a hybrid ML algorithm is also used. Enhancing the offloading latency, synchronization time, and turnaround time is necessary if you want to be able to recognize assaults in real time. © 2024 IEEE.

关键词： Network intrusion

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Interactive Sign Language Learning System Using computer Vision and Deep Learning

Interactive Sign Language Learning System Using Computer Vis...

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Suganiya Murugan Mir Kasif Ali Dhanvanth Singari S. Pradeep Kumar Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai India Electrical and Electronics Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

In an era where communication is key, the gap in accessible tools for those with hearing impairments or speech disabilities is significant. These individuals often face obstacles in education and social interaction due to a heavy reliance on spoken language and a lack of sign language resources. The Interactive Sign Language Learning System (ISLLS) addresses this gap by providing an innovative platform for learning sign language, enhanced with voice output to assist individuals with speech disabilities. This feature allows for auditory feedback alongside visual sign learning, enriching the educational experience. The ISLLS employs advanced technologies like computer vision and deep learning to facilitate sign recognition and text-to-sign conversion. With the new voice output, it further aids those with speech impairments, expanding its inclusivity. This system offers a comprehensive learning tool that caters to a diverse user base, enabling people with speech difficulties to engage more fully with the world. The ISLLS is a significant step towards a more inclusive society, offering a user-friendly platform that not only improves the learning of sign language but also empowers people with speech disabilities to connect and thrive, representing progress in both technology and social inclusivity.

关键词： Learning systems Deep learning Sign language computer vision Visualization Transforms Speech recognition

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Comparative Intrepretation of Machine Learning Algorithms in Predicting the Cardiovascular Death Rate for Covid-19 Data 2

Comparative Intrepretation of Machine Learning Algorithms in...

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2nd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2021

作者： Krithika, D.R. Rohini, K. Vels Institute of Science Technology and Advanced Studies Department of Computer Science Chennai India

ISBN: (纸本)9781665429214

Every year 31% of people die from cardiovascular disease worldwide. The big data analytics technique is very useful to Identify Heart disease and COVID-19. To control the COVID-19 spread around the world and many of the companies adapting this technology and also remote places patient reports doctors view easily to analyze health condition of the patient using IOT based big data. In 2019 COVID-19 (Novel coronavirus Disease) was recognized. COVID-19 signs of CT scan include pleural thickening and vascular enlargement. Nucleic acid detection and epidemiological tracing are using Chest CT scans counteract. To understanding of the disease COVIDE-19 the Researchers are using ML, AI (Artificial Intelligence) and natural language processing. We are using big data analytics to track the spread of this coronavirus. In this paper we discuss about Comparison of Tools in Big data Analytics using machine learning Algorithm. © 2021 IEEE.

关键词： Data Analytics

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Precision Pig Farming Image Analysis Using Random Forest and Boruta Predictive Big Data Analysis Using Neural Network and K- Nearest Neighbor 2

Precision Pig Farming Image Analysis Using Random Forest and...

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2nd International Conference on Intelligent Engineering and Management, ICIEM 2021

作者： Shaik Mazhar, S.A. Suseendran, G. Vels Institute of Science Technology Advanced Studies Department of Computer Science Chennai India

ISBN: (纸本)9781665414500

Conditions and monitoring for production are significant issues in livestock accuracy Agriculture, in which image measurement and smart data collection are required. Dynamical surveillance and review of this Article Man are suggested as a device for scientific identification and growth evaluation of pigs. The Watershed enhanced algorithm is adapted to each human animal's section in chronic occlusion, depending on the depth of the photos captured during flight Camera in the chosen area of interest. For swine's weight, the rate of development is calculated from the image-based calculations and predicted using a segmented linear fitting form. Related results will then be used to interpret and explain incidents. As real-time feedback to the farmers, it happens in the pig hen. Preliminary studies have demonstrated a high potential for precision farming methods for livestock farming to increase efficiency and animal health. In this paper, Machine Learning is used in IMAGE analysis using Random forest and boruta with Predictive Big Data analysis on the pig farming data using the neural network and k- nearest neighbor algorithm for advanced predictive data analysis of our pig farming agriculture. © 2021 IEEE.

关键词： Decision trees

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Computational Modeling of Streptococcus Suis Dynamics via Stochastic Delay Differential Equations

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computer Modeling in Engineering & sciences 2025年第4期143卷 449-476页

作者： Umar Shafique Ali Raza Dumitru Baleanu Khadija Nasir Muhammad Naveed Abu Bakar Siddique Emad Fadhal Department of Mathematics National College of Business Administration and EconomicsLahore54000Pakistan Center for Research in Mathematics and Applications(CIMA) Institute for Advanced Studies and Research(IIFA)University ofÉvoraRua Romão Ramalho59Évora7000-671Portugal Department of Computer Science and Mathematics Lebanese American UniversityBeirut1102-2081Lebanon Department of Zoology University of SialkotSialkot51040Pakistan Department of Mathematics Air UniversityIslamabad44000Pakistan Department of Mathematics and Statistics College of ScienceKing Faisal UniversityAl Ahsa31982Saudi Arabia Department of Physical Sciences The University of ChenabGujrat50700Pakistan

Streptococcus suis(***)is a major disease impacting pig farming *** can also be transferred to humans by eating raw pork.A comprehensive study was recently carried out to determine the indices throughmultiple geographic regions in ***:The well-posed theorems were employed to conduct a thorough analysis of the model’s feasible features,including positivity,boundedness equilibria,reproduction number,and parameter *** Euler,Runge Kutta,and EulerMaruyama are some of the numerical techniques used to replicate the behavior of the streptococcus suis infection in the pig ***,the dynamic qualities of the suggested model cannot be restored using these ***:For the stochastic delay differential equations of the model,the non-standard finite difference approach in the sense of stochasticity is developed to avoid several problems such as negativity,unboundedness,inconsistency,and instability of the *** from traditional stochastic methods either converge conditionally or diverge over *** stochastic non-negative step size convergence nonstandard finite difference(NSFD)method unconditionally converges to the model’s true ***:This study improves our understanding of the dynamics of streptococcus suis infection using versions of stochastic with delay approaches and opens up new avenues for the study of cognitive processes and neuronal *** interaction behaviour and new solution comparison profiles.

关键词： Streptococcus suis disease model stochastic delay differential equations(SDDEs) existence and uniqueness Lyapunov function stability results reproduction number computational methods

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Intelligent Kidney Abnormality Detection in CT Images Using GLCM and Neural Networks 4

Intelligent Kidney Abnormality Detection in CT Images Using ...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Bajpai, Chhavi Anitha, S. Gaur, Manish Raghini, M. Kaliappan, S. Khan, Imran Centre for Advanced Studies Aktu Department of Computer Science and Engineering Lucknow India Acs College of Engineering Department of CSE-Cyber Security Bangalore India Institute of Engineering and Technology Aktu Department of Computer Science & Engineering Lucknow India Velammal College of Engineering and Technology Department of Artificial Intell igence and Data Science Madurai India Lovely Professional University Department of Research and Development Jalandhar India Sharda School of Nursing Science and Research Sharda University Department of Medical Surgical Nursing Greater Noida India

ISBN: (纸本)9798350352931

Kidney stone illness, one of the most common medical problems worldwide, relieves pain and drives people to the ER. Diagnoses often use imaging, but educated doctors must interpret them. computer-aided diagnostics (CAD) systems help doctors discover kidney stones more accurately. The suggested technique includes preprocessing, feature extraction, and model training. Ultrasound (US) image preprocessing includes restoration, smoothness, sharpness, and contrast enhancement. The Grey Level Co-occurrence Matrices (GLCM) analyze image texture to extract characteristics. Model training uses KELM. The KELM-based model outperforms Extreme Learning Machine (ELM) and K-Nearest Neighbours (KNN) with an average accuracy of 92.54%. This accuracy demonstrates the method can detect kidney stones in ultrasound images. This proposed shows that computer-aided kidney stone detection can be fast and accurate. The combination of GLCM's texture analysis and KELM's classification skills can help medical professionals diagnose kidney stones using imaging. © 2024 IEEE.

关键词： computerized tomography

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