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electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： Deepa R Manisha Das Thilakavathy P Bennet Prabhu A Surendran R Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai India Department of CSE(AI&ML) GMR Institute of Technology Rajam Andhrapradesh India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India KONE Elevator India Technology & Engineering Centre Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

This paper introduces Agricultural Advancement using NLP (AA-NLP) techniques as a strategy to overcome the limitations of traditional agricultural management information systems. There is a lot of unstructured data available but this situation makes agricultural research inefficient because of its knowledge extraction and usage failure. This problem is addressed by combining an ontology framework developed for agricultural information management with Natural Language Processing (NLP) methods. The solution, AA-NLP, utilizes modern NLP algorithms which extract, classify and evaluate agricultural information from many textual sources that include research papers, reports and online repositories. AA-NLP achieves effective information retrieval and exploitation through named entity identification, sentiment analysis and semantic similarity as well as other techniques to convert unstructured input into structured ontological representations. Applying AA-NLP has several benefits such as increased discoverability of relevant agricultural information, automatic classification of scientific findings, and generation of actionable insights for decision making in farming practices, crop management and formulation of agricultural policies. The effectiveness of AA-NLP was analyzed in this paper by measuring the precision associated with knowledge extraction and ontology creation through various performance metrics including accuracy, sustainability farming practices, agricultural productivity and precision.

关键词： Productivity Sentiment analysis Semantics Ontologies Information retrieval Information management Data mining Stakeholders Sustainable development Farming

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Millimeter-sized, ultra-sensitive planar Hall effect gradiometer

Millimeter-sized, ultra-sensitive planar Hall effect gradiom...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Hariharan Nhalil Moty Schultz Shai Amrusi Asaf Grosz Lior Klein Department of Physics Institute of Nanotechnology and Advanced Materials Bar-Ilan University Ramat-Gan Israel Department of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva Israel

ISBN: (数字)9798350338362

ISBN: (纸本)9798350338379

We present a miniature, ultra-sensitive magnetic field gradiometer in the form of a single elliptical planar Hall effect sensor that allows measuring magnetic field at 9 different locations on a 4 mm length scale [1]. The gradiometer detects magnetic field gradients with equivalent gradient magnetic noise levels of $\sim 958, \sim 192, \sim 51$ , and $\sim 26\ \text{nT}/\mathrm{m}\surd$ Hz at 0.1, 1, 10, and 50 Hz, respectively, and tested under ambient conditions by measuring the field gradient produced by an electric current flowing through a straight wire. The compact size, low noise level, versatility, simple design, and low cost of this gradiometer makes it a suitable choice for detecting magnetic field gradients in small, confined spaces such as current probes or wearable electronic medical devices.

关键词： Magnetic field measurement Current measurement Magnetic confinement Wires Magnetic noise Hall effect Magnetic fields

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Energy-Efficient Load Balancing Technique to optimize Average response time and Data Center Processing Time in Cloud Computing Environment 2

Energy-Efficient Load Balancing Technique to optimize Averag...

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2nd International Conference on Physics and Applied Sciences, ICPAS 2021

作者： Jeyalaksshmi, S. Anita Smiles, J. Akila, D. Mukherjee, Dibyendu Obaid, Ahmed J. Department of Information Technology Vels Institute of Science Technology and Advanced Studies Chennai India Chennai India Department of Computer Science and Engineering Brain Ware University India Faculty of Computer Science and Mathematics University of Kufa Iraq

Cloud infrastructure is a modern computing system in which pooled services are made available to users at various times depending on their demands. The process of distributing workload among computing system nodes is known as load balancing. Loads include things like CPU power;ram capacity, and network traffic. A well-balanced load avoids the situation when some nodes are fully loaded while others are inactive or not working. Where there are many tasks in a virtual machine (VM), these tasks are delegated to underutilize VMs in the same or a separate datacenter. Modified Round Robin and Modified Honey Bee Algorithms are proposed in this article for effective load balancing based on honey bee and Round Robin foraging activity to control load through VMs. Tasks taken from VMs that are crowded are viewed as honey bees. In the suggested method which is a circular ribbon, filled VMs are considered. In order to ensure a fast responding time and a minimum number of task migrations, the planned protocol also explores the aims of tasks in VM queues. The results of the test indicate that the quality of service has improved considerably (QoS). © Published under licence by IOP Publishing Ltd.

关键词： Quality of service

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Long Short-term Fusion by Multi-scale Distillation for Screen Content Video Quality Enhancement

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Huang, Ziyin Chan, Yui-Lam Kwong, Ngai-Wing Tsang, Sik-Ho Lam, Kin-Man Ling, Wing-Kuen The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Hong Kong Hong Kong Chu Hai College Department of Computer Science Hong Kong Hong Kong Tsientang Institute for Advanced Study Center for Integrated Circuits and Artificial Intelligence Zhejiang China

Different from natural videos, where artifacts distributed evenly, the artifacts of compressed screen content videos mainly occur in the edge areas. Besides, these videos often exhibit abrupt scene switches, resulting in noticeable distortions in video reconstruction. Existing multiple-frame models using a fixed range of neighbor frames face challenges in effectively enhancing frames during scene switches and lack efficiency in reconstructing high-frequency details. To address these limitations, we propose a novel method that effectively handles scene switches and reconstructs high-frequency information. In the feature extraction part, we develop long-term and short-term feature extraction streams, in which the long-term feature extraction stream learns the contextual information, and the short-term feature extraction stream extracts more related information from shorter input to assist the long-term stream to handle fast motion and scene switches. To further enhance the frame quality during scene switches, we incorporate a similarity-based neighbor frame selector before feeding frames into the short-term stream. This selector identifies relevant neighbor frames, aiding in the efficient handling of scene switches. To dynamically fuse the short-term feature and long-term features, the muti-scale feature distillation focuses on adaptively recalibrating channel-wise feature responses to achieve effective feature distillation. In the reconstruction part, a high-frequency reconstruction block is proposed for guiding the model to restore the high-frequency components. Experimental results demonstrate the significant advancements achieved by our proposed Long Short-term Fusion by Multi-Scale Distillation (LSFMD) method in enhancing the quality of compressed screen content videos, surpassing the current state-of-the-art methods. © 2025 IEEE.

关键词： Image reconstruction

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CNN-LSTM: Development of Offline Signature Authentication

CNN-LSTM: Development of Offline Signature Authentication

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Emerging Research in Computational Science (ICERCS), International Conference on

作者： Revathy Ganapathy Lingeshwaran D P. SuthanthiraDevi Harish Kumar M V Department of Computer Science and Engineering Vels Insitute of Science Technology and Advanced Studies Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai India

Offline Signature Authentication is a critical task in the field of document authentication, and its accuracy is essential for ensuring security while transactions. This research proposes two approaches: Initially Pre-trained CNN models are used to extract features from signature images, which are then combined with handcrafted features such as HOG and some other geometric features of signature. Such combined features are passed to bidirectional LSTM model in which drop out layer undergoes classification which differentiate real and forgery signature. The proposed system has potential applications in document authentication and security, subsequently combination of CNN models and additional features provides more comprehensive representation of signature images resulting in improved accuracy. Three signature datasets are utilized namely GDPS, CEDAR, and BHSig-Bengali each with varying signature styles and image quality. Our experimental outcomes reveal that Bidirectional Convolutional LSTM along with handcraft features attained maximum accuracy in offline signature verification system.

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A Survey of the AVOA Metaheuristic Algorithm and its Suitability for Power System Optimization and Damping Controller Design

A Survey of the AVOA Metaheuristic Algorithm and its Suitabi...

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International eConference on computer and Knowledge engineering (ICCKE)

作者： Aliyu Sabo Theophilus Ebuka Odoh Samuel Habu Hossien Shahinzadeh Farshad Ebrahimi Department of Electrical andElectronic Engineering Advanced Lightning Power and EnergyResearch (ALPER) Universiti Putra Malaysia Serdang Malaysia Department of Electrical Engineering Water Resource Institute Kaduna Nigeria Department of Electrical Engineering Amikabir University of Technology Tehran Iran Dept. of Electrical and Computer Engineering University of Houston Houston TX USA

The performance of metaheuristic optimization techniques is system-dependent, it is essential to ascertain the suitability of an algorithm before its application. This study surveys the suitability of AVOA in damping controller design by reviewing the performance of AVOA in used cases of power system optimization problems and damping controller design. Based on the findings, AVOA is recommended as a good optimizer in power system optimization and damping controller design.

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An Effective Diagnosis of Alzheimer’s Disease with the Use of Deep Learning based CNN Model

An Effective Diagnosis of Alzheimer’s Disease with the Use ...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： K C Sharmili G P Suja E. Pandian Md. Abul Ala Walid Sripriya Arunachalam G.Charles Babu Computer Science Stella Maris College 17 Cathedral Road Chennai Computer science Muslim Arts College Thiruvithancode Master of Computer Application Sri Muthukumaran Institute of Technology Mangadu Chennai Department of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Khulna Computer Applications-PG School of Computing Sciences VELS Institute of Science Technology & Advanced Studies - VISTAS Velan Nagar P.V. Vaithiyalingam Road Pallavaram Chennai Department of Computer Science and Engineering GRIET Hyderabad

In recent years, Alzheimer’s disease has become a major health concern. Over 45 million individuals worldwide suffer from this illness. Alzheimer’s disease is a neurodegenerative illness of unidentified etiology and process that causes cognitive deterioration, and it mostly affects the elderly. Dementia, the gradual and irreversible loss of brain cells, is the leading cause of Alzheimer’s disease. Those with this illness lost the ability to think, read, and do other basic tasks. A machine learning system may aid in this situation by making accurate predictions about the onset of illness. Dementia diagnosis in a broad patient population is the major objective. This research presents the findings and analysis of many Machine Learning models for identifying dementia. To alleviate this obstacle and help in the diagnosis of AD will create a deep-learning architecture that uses stacked auto-encoders and a softmax output layer. The proposed technique can analyze numerous classes in a single setting, unlike earlier procedures, and only needs a small number of labeled training samples and basic domain expertise to get started. In the present studies, a substantial improvement in performance when it came to classifying all diagnostic subgroups. Using the proposed technique can run these time series through a Convolutional Neural Network (CNN) and ResNet50 model to extract the distinguishing patterns across stages. With an F1-score of 0.99 and an accuracy of 99.91 percent, the CNN-based technique outperformed the standard feature-based classifiers by a significant margin.

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Intelligent Malicious URL Detection Using Kernel PCA-SVM-GA Model with Feature Analysis

Intelligent Malicious URL Detection Using Kernel PCA-SVM-GA ...

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Data Science and Network Security (ICDSNS), International Conference on

作者： Sandy Ariawan Arvind Kumar Chinthala Kumara Swamy V. Divya V Manikandan S. Rukmani Devi IAKN Tarutung Indonesia SCA Manav Rachna International Institute of Research and Studies Faridabad India CSE-DS Mallareddy College of Engineering Hyderabad India Department of Information Technology Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India Department of Computer Science and Engineering Jain University Bangalore India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Dangerous websites, or rogue URLs, pose a serious threat to cybersecurity. Malicious URLs cost businesses and consumers alike billions of dollars annually because the system lure unsuspecting users into scams that steal personal information, cause financial loss, or install malware. Immediate action is required to detect and eliminate these threats. This form of detection has traditionally relied on blacklists. A major drawback of blacklists is that the system are not comprehensive and do not detect newly established harmful URLs. The procedure consists of three stages, which are preprocessing, feature extraction, and training the model. Important parts of data preprocessing when dealing with URLs are standardization and parsing. When it comes to detecting dangerous URLs using machine learning, feature extraction is a major concern. The proposed approach used a Kernal PCA-SVM-GA to train the model. The proposed approach beats both SVM and GA, with an average accuracy of 93.52%.

关键词： Uniform resource locators Training Support vector machines Accuracy Data preprocessing Standardization Machine learning Feature extraction Vectors Blocklists

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Simultaneous Estimation of Tonic Dopamine and Serotonin with High Temporal Resolution In Vitro Using Deep Learning

Simultaneous Estimation of Tonic Dopamine and Serotonin with...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Seongtak Kang Yunho Jeong Ji-Woong Choi Department of Electrical Engineering and Computer Science Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu Republic of Korea College of Transdisciplinary Studies DGIST Daegu Republic of Korea Department of Electrical Engineering and Computer Science DGIST Daegu Republic of Korea Brain Engineering Convergence Research Center DGIST Daegu Republic of Korea

Quantitative measurement of the phasic (changes in several seconds) and tonic (changes in minutes to hours) level changes of neurotransmitters is an essential technique for understanding brain functions and brain diseases regulated by the neurotransmitters. However, monitoring phasic and tonic levels of multiple neurotransmitters is still a challenging technology. Microdialysis can measure the tonic levels of multiple neurotransmitters simultaneously but has a low temporal resolution (minute) to analyze precisely. Fast-scan cyclic voltammetry (FSCV) has high temporal resolution and high sensitivity, but it was not able to simultaneously measure the tonic level of multiple neurotransmitters. The recently proposed deep learning-based FSCV method was still only capable of phasic concentration estimation of neurotransmitters. In this study, we estimate the tonic levels of dopamine and serotonin simultaneously by training a deep-learning network with the extracted tonic information from the FSCV. The proposed deep learning model was validated in vitro to simultaneously estimate tonic concentrations of two neurotransmitters with statistically significantly higher accuracy than previously proposed background subtraction-based models (p<0.001). In particular, in the case of serotonin concentration estimation error, the proposed model showed higher prediction performance than the background subtraction-based model (48 nM and 73 nM, respectively). We expect that the proposed technique will help simultaneous measurement of the phasic and tonic levels of numerous neurotransmitters in vivo *** Relevance— This study proposes a method to simultaneously measure tonic dopamine and tonic serotonin with high temporal resolution with a single electrode in the brain.

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Highsimb: A Concrete Blockchain High Simulation with Contract Vulnerability Detection for Ethereum and Hyperledger Fabric 4th

Highsimb: A Concrete Blockchain High Simulation with Contra...

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Huang, Pengfei Jie, Wanqing Voundi Koe, Arthur Sandor Hou, Ruitao Yan, Hongyang Nouioua, Mourad Thien, Phan Duc Mbous Ikong, Jacques Lancine, Camara Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou510006 China Pazhou Lab Guangzhou510330 China Faculty of Mathematics and Computer Science University of Mohamed Bachir El Ibrahimi Bordj Bou Arreridj34030 Algeria Faculty of Information Technology Nam Dinh University of Technology Education Nam Dinh420000 Viet Nam Department of Electrical and Telecommunications Engineering National Advanced School of Engineering - University of Yaounde I Yaounde337 Cameroon Department of Computer Science University of Social Sciences and Management Bamako2575 Mali

ISBN: (纸本)9783031200984

Blockchain testing plays a critical role in the maturation of blockchain technology by ensuring the quality of implemented functional and non-functional requirements. In the new global economy, rapid time to market has become a central issue: developers fail to scrutinize their blockchain designs prior to deployment and customers undergo negative experiences that hurt the widespread adoption of the blockchain technology. Previous published studies aimed for effective blockchain simulators. However, existing solutions exhibit several drawbacks: they rely on guesswork, conceal low-level implementation details, lack expected realistic outcomes and automated testing, as well as lag in smart contract vulnerability analysis. In this paper, we introduce highsimb: the first concrete blockchain high simulation platform for Ethereum and Hyperledger Fabric that supports smart contract vulnerability detection. Unlike a testnet, the blockchain tester can customize any low-level detail to achieve realistic expected results under automated testing. Theoretical analysis demonstrates our concrete simulator is highly observable, supports realistic feedback, is scalable, detects smart contract vulnerabilities, has strong white-box testing capabilities and automates experiments. Our framework complements existing blockchain simulators and introduces a novel development paradigm for blockchain testing. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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