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1st International Conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

作者： Ramkumar, M.P. Rani, J. Lece Elizabeth Jeyarohini, R. Selvan, G. S. R. Emil Konar, Rahul Shiva Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9783031472237

Software-defined networking (SDN), a newly developed architecture that alters the route (paths) of traffic packets, is a flexible and logically centralized control plane that provides a reduction in the network’s energy usage. SDN is designed in such a way that the switches and links can handle a large amount of traffic. Their energy use is not proportional to traffic. Due to the increase in network communication, SDN devices demand much more load and energy. The SDN controller has to be modeled efficiently. The system model of the controller is proposed which efficiently distributes the load. This model helps in traffic routing with minimal energy consumption for networking devices. In addition to the above, it balances the load based on the heterogeneous traffic distribution. The proposed method optimizes the energy consumed by the networking devices by means of using the few active switches, minimizing the number of device state transitions, and handling the volume of traffic. Load balancing is performed for multiple controllers. The switch migration technique is used for migrating the traffic from one controller to the other. Energy-efficient routing is done by the proposed algorithm with reduced device state transition. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Software defined networking

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Risk Factors Categorizations of Ischemic Heart Disease in South-Western Bangladesh

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Data Intelligence 2024年第3期6卷 834-868页

作者： M.Raihan Sami Azam Laboni Akter Md.Mehedi Hassan Ryana Quadir Asif Karim Saikat Mondal Arun More Department of Computer Science and Engineering North Western UniversityKhulna 9100Bangladesh Faculty of Science and Technology Charles Darwin UniversityNT 0909Australia Departmentof Biomedical Engineering Khulna University of Engineering&TechnologyKhulnaBangladeh Computer Science and Engineering Discipline Khulna UniversityKhulna 9208Bangladesh Department of Computer Science and Engineering Daffodil International University(DIU)Bangladesh Department of Cardiology Ter Institute of Rural Health and ResearchMurud-413510India

Ischemic heart disease(IHD)is one of the leading causes of death ***,different geographic regions show different variations of the risk factors of this disease based on the different lifestyles of *** study examines the current IHD condition in southern Bangladesh,a Southeast Asian middle-income *** main approach to this research is an Al-based proposal of a reduced set of the greatest impact clinical traits that may cause *** approach attempts to reduce IHD morbidity and mortality by early detection of risk factors using the reduced set of clinical ***,diagnostic,and symptomatic features were considered for analysing this clinical *** pre-processing utilizes several machine learning techniques to select significant features and make meaningful interpretations.A proposed voting mechanism ranked the selected 138 features by their impact *** this regard,diverse patterns in correlations with variables,including age,sex,career,family history,obesity,etc.,were calculated and explained in terms of voting *** the 138 risk factors,three labels were categorized:high-risk,medium-risk,and low-risk features;19 features were regarded as high,25 were medium,and 94 were considered low impactful *** research's technological methodology and practical goals provide an innovative and resilient framework for addressing IHD,especially in less developed cities and townships of Bangladesh,where the general population's socioeconomic conditions are often *** data collection,pre-processing,and use of this study's complete and comprehensive IHD patient dataset is another innovative *** believe that other relevant research initiatives will benefit from this work.

关键词： Ischemic heart disease Machine learning CVD Data categorization Medical data

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Identification of Tamil Sign Language using Hamiltonian deep neural network

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International Journal of Systems, Control and communications 2025年第1期16卷 62-78页

作者： Lingeswari, Ponnusamy Pavunraj, Daniel Kanagavalli, Mariappan Manikandan, Karuppasamy Department of Electronics and Communication Engineering P.S.R. Engineering College Tamil Nadu Sivakasi India Department of Artificial Intelligence and Data Science Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering P.S.R.R. College of Engineering Tamil Nadu Sivakasi India Department of Computer Science and Engineering P.S.R. Engineering College Tamil Nadu Sivakasi India

Sign language consisting of various hand patterns, serves as a vital communication medium for conveying messages, sharing knowledge, and expressing ideas among the deaf community. This research addresses the challenge of recognising Tamil Sign Language (TSL) by proposing a novel identification method using a Hamiltonian deep neural network (HDNN). The primary objective is to maximise the accuracy and reliability of TSL recognition, especially for real-time applications. The dataset comprises real-time images of 12 vowels, 1 Aayutha Ezhuthu, and 18 consonants, collected from 110 different signers. To address noise in the images, an iterative guided filtering (IGF) method is employed for preprocessing. Subsequently, feature extraction is performed using residual exemplars local binary pattern (RELBP). HDNN is then applied to classify the signs. The performance of the proposed approach achieves 23.32%, 25.07%, 21%, 27.53%, and 30% higher accuracy and 14.09%, 19.63%, 28%, 18.45%, and 10.54% lower error rates compared to existing methods. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Local binary pattern

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Design of a Multi-Stage Ensemble Model for Thyroid Prediction Using Learning Approaches

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Intelligent Automation & Soft Computing 2024年第1期39卷 1-13页

作者： M.L.Maruthi Prasad R.Santhosh Department of Computer Science and Engineering Faculty of EngineeringKarpagam Academy of Higher EducationCoimbatoreTamil NaduIndia

This research concentrates to model an efficient thyroid prediction approach,which is considered a baseline for significant problems faced by the women *** major research problem is the lack of automated model to attain earlier *** existing model fails to give better prediction ***,a novel clinical decision support system is framed to make the proper decision during a time of *** stages are followed in the proposed framework,which plays a substantial role in thyroid *** steps include i)data acquisition,ii)outlier prediction,and iii)multi-stage weight-based ensemble learning process(MS-WEL).The weighted analysis of the base classifier and other classifier models helps bridge the gap encountered in one single classifier *** classifiers aremerged to handle the issues identified in others and intend to enhance the prediction *** proposed model provides superior outcomes and gives good quality prediction *** simulation is done in the MATLAB 2020a environment and establishes a better trade-off than various existing *** model gives a prediction accuracy of 97.28%accuracy compared to other models and shows a better trade than others.

关键词： Thyroid machine learning pre-processing classification prediction rate

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RouteNow: Ensuring Least Hop Routing towards SDIoT based Backup Hospital Networks

RouteNow: Ensuring Least Hop Routing towards SDIoT based Bac...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Sarkar, Kounteya Misra, Sudip Sonu, Mayank Iit Department of Computer Science and Engineering Kharagpur India Iit Department of Electronics and Electrical Communication Engineering Kharagpur India

ISBN: (纸本)9798350351255

This work proposes routeNow, a routing algorithm designed for wireless Software Defined Internet of Things by dynamic path relaxation based on real-time heuristic that always gives the best least hop path. This is aimed towards providing an alternative backup network for a smart hospital that functions independently on the failure of the primary hospital network. Given a pair of source and destination nodes, a central controller with a global network view first calculates a minimum hop path greedily by minimizing the remaining distance to destination. Hence, the path so found is relaxed dynamically by substituting certain links with other links keeping the hop count constant if a new path is found with a lesser latency than the previous one. For this, the controller maintains a matrix containing real-time delay between neighboring IoT nodes given the current network state and whose values are periodically refreshed using a heuristic function based on actual delay measurements. This enables the controller to dynamically modify routing paths. Simulation analysis of routeNow shows that it formulates routing paths with significant less time even with large networks and by always maintaining a route with least hops, on average a least latency path is also ensured. © 2024 IEEE.

关键词： Internet of things

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Investigating the Ethical Implications of Artificial Intelligence and Establishing Guidelines for Responsible AI Development

Investigating the Ethical Implications of Artificial Intelli...

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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Sukumar, R. Gambhir, Vinima Seth, Jyoti Faculty of Engineering and Technology Department of Electronics and Communication Engineering Bangalore India Atlas SkillTech University Department of Isme Mumbai India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350388800

Now more than ever, artificial intelligence (AI) presents both unprecedented potential and difficulties. This article delves deeply into the ethical concerns raised by AI and lays forth principles that should be followed in the creation of ethical AI systems. In the opening, we recognize the revolutionary potential of AI as well as the ethical quandaries it poses. In order to guarantee that AI technologies are created and implemented in a responsible and fair way, a deep ethical examination is required in light of the proliferation of autonomous systems. To address these ethical difficulties, we suggest the adoption of an Ethical Analysis Framework (EAF). The Ethical Assessment Framework (EAF) provides a framework for systematically assessing the fairness, transparency, privacy, accountability, and bias of AI systems. It's a cornerstone for the community of AI ethics researchers, developers, and policymakers working together to keep AI honest. Our approach places special importance on the gathering and preparation of data. We emphasize how important ethically sound data is in defining the moral implications of AI. We found that when compared to other approaches to assessing the morality of AI, our suggested technique consistently outperformed the others. The suggested technique establishes itself as an innovative and effective way of navigating the growing ethical environment by providing a systematic approach to addressing AI's ethical implications and supporting responsible AI research. For instance, as compared to more conventional methods, our technique increased fairness evaluations by an average of 15%. In conclusion, there is a wide range of concerns related to the morality of AI that must be constantly monitored and addressed via joint effort. Our method places an emphasis on making sure AI is progressing in a way that is responsible, ethical, and fair to all members of society. © 2024 IEEE.

关键词： Ethical technology

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Dual-Channel MEMS Actuated Tunable Optical Filter Based on Polarization Insensitive Compound Ring Resonator for DWDM Networks 11th

Dual-Channel MEMS Actuated Tunable Optical Filter Based on P...

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11th EAI International Conference on Advancement of science and Technology, ICAST 2023

作者： Ayalew, Hailu Dessalegn Talabattula, Srinivas Annamalai, Pushparaghavan Electrical and Computer Engineering Faculty Bahir Dar University-Institute of Technology Bahir Dar Ethiopia Department of Electrical Communication Engineering Indian Institute of Science Bangalore India

ISBN: (纸本)9783031641503

One of the key components of wavelength division multiplexing (WDM) networks is tunable optical filters (TOFs). This paper focused on the theoretical analysis and design of a new dual-channel micro-electromechanical system (MEMS) actuated tunable optical filter based on polarization insensitive compound ring resonators (PICRRs) for dense wavelength division multiplexing (DWDM) optical networking applications. It is basically structured on an optimally designed capacitive type microcantilever beam, which is fixed at both ends, with built-in PICRRs, which are placed at both fixed ends of the beam to gain linearly varying longitudinal strain and stress on the resonators. Also, the dimensions of the rings are properly chosen to achieve a full width at half maximum (FWHM) of 0.4078 nm with a flat pass band at 1 dB of 0.3626 nm and a good shape factor for both channels. It also shows a free spectral range of about 20.05 nm for the first channel and 20.046 nm for the second channel, with a high extinction ratio that allows filtering of adjacent channels of 50 GHz DWDM systems. Thus, when the electrostatic attraction force (EAF) is applied, the beam will bend, which induces strain and stress in the rings, which brings a change in effective refractive index of the rings and also affects the perimeter and coupling coefficients of the rings that leads to a change in the output spectrum shift, providing the tunability as high as 4.779 nm/μN, and it is capable of tuning up to 355 GHz with negligible variations of FWHM and channel spacing within the tunability range of the two channels. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dense wavelength division multiplexing

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Document Summarizer: A Machine Learning approach to PDF summarization 4

Document Summarizer: A Machine Learning approach to PDF summ...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Peddarapu, Rama Krishna Anogna, Sudagoni Harshini, M.N.S. Sri Sireesha, J. Rathna, K. Guna Department of Computer Science and Engineering Faculty of Computer Sciences and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India

To address the need for summarizing and extracting information efficiently, this paper highlights the growing challenge posed by the increasing number of PDF files. Reading lengthy documents is a tedious and time-consuming task. To save time and quickly comprehend the key points of a PDF, a PDF summarizer tool has been developed to tackle these issues. In today's professional environments, gathering and managing data from documents is critical. This article introduces an innovative solution called DocSum, which automates the process of summarizing extracted data. Powered by *** Core, the DocSum system allows users to upload PDF documents for processing and receive concise summaries in return. The system features a user-friendly interface that encourages interaction and engagement, utilizing AI and machine learning techniques to streamline document handling. Users can request specific summaries, enabling efficient document management workflows. By empowering users to seamlessly interact with vast amounts of information, DocSum enhances productivity and explores new ways to optimize document management. This solution is ideal for professionals seeking to stay informed and manage data effectively, while also keeping track of advancements in document handling technologies across multiple industries. It is not only helpful for professionals but also helpful for students who need quick revision or examinations, teachers to gain brief knowledge on the concepts. © 2025 The Authors. Published by Elsevier B.V.

关键词： Data handling

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The Human Eye Pupil Detection System Using BAT Optimized Deep Learning Architecture

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computer Systems science & engineering 2023年第7期46卷 125-135页

作者： S.Navaneethan P.Siva Satya Sreedhar S.Padmakala C.Senthilkumar Department of Electronics and Communication Engineering Saveetha Engineering CollegeChennai602105India Department of Information Technology Seshadri Rao Gudlavalleru Engineering CollegeKrishna521356India Department of Computer Science Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Electronics and Communication Engineering Saveetha School of EngineeringSIMATSChennai602105India

The pupil recognition method is helpful in many real-time systems,including ophthalmology testing devices,wheelchair assistance,and so *** pupil detection system is a very difficult process in a wide range of datasets due to problems caused by varying pupil size,occlusion of eyelids,and *** Convolutional Neural Networks(DCNN)are being used in pupil recognition systems and have shown promising results in terms of *** improve accuracy and cope with larger datasets,this research work proposes BOC(BAT Optimized CNN)-IrisNet,which consists of optimizing input weights and hidden layers of DCNN using the evolutionary BAT algorithm to efficiently find the human eye pupil *** proposed method is based on very deep architecture and many tricks from recently developed popular *** results show that the BOC-IrisNet proposal can efficiently model iris microstructures and provides a stable discriminating iris representation that is lightweight,easy to implement,and of cutting-edge ***,the region-based black box method for determining pupil center coordinates was *** proposed architecture was tested using various IRIS databases,including the CASIA(Chinese academy of the scientific research institute of automation)Iris V4 dataset,which has 99.5%sensitivity and 99.75%accuracy,and the IIT(Indian Institute of Technology)Delhi dataset,which has 99.35%specificity and MMU(Multimedia University)99.45%accuracy,which is higher than the existing architectures.

关键词： BAT algorithm IRIS datasets DCNN pupil detection black box method

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A Novel Hybrid CNN-LSTM Framework with Ant Colony Optimization for Robust Fake News Detection

A Novel Hybrid CNN-LSTM Framework with Ant Colony Optimizati...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Santosh Kumar, B. Viji, M. Gowtham, T. Sekar, G. Sharmila, S. Saravanan, M. New Horizon College of Engineering Department of Computer Science and Engineering Bengaluru560103 India E.G.S. Pillay Engineering Engineering Department of Computer Science and Business Systems Nagapattinam611002 India P.A. College of Engineering and Technology Department of Electronics and Communication Engineering Pollachi642002 India Vsb College Of Engineering Department of Electronics and Communication Engineering Technical Campus Coimbatore642109 India Sir Issac Newton College of Engineering and Technology Department of Computer Science and Engineering Nagapattinam6111002 India Sri Eshwar college of engineering Department of Electronics and Communication Engineering Coimbatore641202 India

ISBN: (纸本)9798331509828

In the digital age, the spread of fake news has become ubiquitous: it weakens public speech and weakens public trust. In this paper we propose a novel method of detecting fake news using a deep learning framework. To do text analysis, we propose a hybrid model that incorporates Convolutional Neural Network and Long Short-Term Memory Network by utilizing on the strengths of each for effective text analysis. However, the LSTM layer is good in learning the context and sequence of the text, and the CNN component beats in extracting local and spatial details in textual data. The effectiveness of the proposed work is tested on a comprehensive data set of news sources (covering different sources, from different countries, and labeled for authenticity). We trained and evaluated the model on this dataset which mainly included features of linguistic and semantic nature to differentiate between genuine and fake news. We show that our results exhibit the biggest accuracy, precision, recall, and Fl-score improvement over old models. Automated fake news detection is a growing field, and this research proposes a robust and scalable solution to cut off the spread of misinformation. © 2025 IEEE.

关键词： Convolutional neural networks

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