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International Conference on Inventive Computation technologies (ICICT)

作者： T P Pushpavathi Santhosh Kumari N K Kubra MS Ramaiah University of Applied Sciences Bengaluru Karnataka India M.Tech in Computer Science and Networking MS Ramaiah University of Applied Sciences Bengaluru Karnataka India

ISBN: (数字)9781728185019

ISBN: (纸本)9781728185026

Heart disease is one of the major cause of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the field of clinical data analysis. With the advanced development in machine learning (mL), artificial intelligence (AI) and data science has been shown to be effective in assisting in decision making and predictions from the large quantity of data produced by the healthcare industry. mL approaches has brought lot of improvements and broadens the study in medical field which recognizes patterns in the human body by using various algorithms and correlation techniques. One such reality is coronary heart disease, various studies gives impression into predicting heart disease with mL techniques. Initially mL was used to find degree of heart failure, but also used to identify significant features that affects the heart disease by using correlation techniques. There are many features/factors that lead to heart disease like age, blood pressure, sodium creatinine, ejection fraction etc. In this paper we propose a method to finding important features by applying machine learning techniques. The work is to design and develop prediction of heart disease by feature ranking machine learning. Hence mL has huge impact in saving lives and helping the doctors, widening the scope of research in actionable insights, drive complex decisions and to create innovative products for businesses to achieve key goals.

关键词： Heart machine learning algorithms machine learning medical services Predictive models Prediction algorithms Diseases

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Securing Smart microgrids: A Cybersecurity Survey

Securing Smart Microgrids: A Cybersecurity Survey

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Prabha Shreeraj Nair Sanjeev Kumar mandal Neeraj Sharma Department of IT and M.Tech Integrated Noida Institute of Engineering & Technology Greater Noida Uttar Pradesh India Computer Science Engineering Department Jain (Deemed to be University) Bangalore India Department of Electrical Engineering Vivekananda Global University Jaipur India

The cybersecurity concerns of intelligent microgrids are thoroughly investigated in this scholarly work. The intricate link that exists between the cyber grid and smart grid operating processes is examined, exposing the cyber system's susceptibility to different types of attacks. The research highlights the potentially catastrophic consequences, both materially and financially, that might result from hacks that jeopardize the operation of these smart microgrids. Particularly in islanded modes with little inertia, hackers are more likely to affect power electronics-intensive smart small power plants and seriously jeopardize their operational stability. The study starts with a brief synopsis of digital and physical systems in wise micro-grids before delving further into cyberattacks that aim to compromise data availability, confidentiality, and integrity. The research categorizes defensive methods into two groups in order to counter FDI attacks: protection plans, which include safeguarding certain meter readings, and surveillance/mitigation strategies, which comprise perhaps static or dynamic detection techniques. Examples of how to create, detect, and mitigate FDI attacks in smart microgrids are provided. The paper also includes a list of critical cybersecurity guidelines pertaining to smart electricity networks and notable instances of ongoing cybersecurity projects carried out globally. The essay concludes with a discussion of future directions in smart microgrid cybersecurity.

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A Crucial Review on Ai Use In Hybrid Grids: A Ultra Smart Way Towards micro Grid

A Crucial Review on Ai Use In Hybrid Grids: A Ultra Smart Wa...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Amandeep Gill Abdul Khalid murugan R Department of R&D Cell Vivekananda Global University Jaipur India Department of IT and M.Tech Integrated Noida Institute of Engineering & Technology Greater Noida Uttar Pradesh India Computer Science Engineering Department Jain (Deemed to be University) Bangalore India

The smart grid seamlessly integrates modern control, communication, and metering technologies, revolutionising the capture of large-scale, multi-type, high-dimensional data about electric power grid operations. Even with these developments, traditional approaches to modelling, optimisation, and control still face challenges when handling large amounts of data. As such, the noticeable uptick in the use of methods based on AI in the context of smart grids is significant. A thorough analysis is conducted in this study, providing an organised overview of common AI approaches used for important tasks including load estimation, power grid stability evaluation, defect detection, and security issues in smart grids and electrical infrastructure. In addition, the poll sheds light on upcoming research difficulties and imagines how AI technologies will be used to create genuinely intelligent electric grids. Additionally, the study pinpoints chances to use AI to solve smart grid problems. In the end, the article highlights how AI approaches may significantly improve the resilience and dependability of smart grid mechanisms, therefore bringing in an era for flexibility and effectiveness in the field of electric power distribution.

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An Automatic Error Recognition approach for machine Translation Results based on Deep Learning

An Automatic Error Recognition approach for Machine Translat...

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computer, Communication and Control (IC4), International Conference on

作者： m N Rekha mudarakola Lakshmi Prasad Saptarshi mukherjee Sudam Vasant Nikam Swati Sharma Pundru Chandra Shaker Reddy Electrical and Electronics Engineering Sri Jayachamarajendra College of Engineering JSS Science and Technology University Mysore Karnataka India Computer Science and Engineering Institute of Aeronautical Engineering Dundigal Telangana India M.Tech-Bioinformatics Maulana Abul Kalam Azad University of Technology West Bengal India Instrumentation Engineering Bharati Vidyapeeth College of Engineering Navi Mumbai India School of Computer Science Engineering and Information Science Presidency University Bangalore Karnataka India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

At present, the speedy growth of natural-language-processing(NLP) has produced considerable evolution for the topic of machine-translation. There have been many deep neural network–based machine-translation(mT) methods that have become increasingly broad. Still, there currently lacks reliable automatic-error-detection(AED) systems for mT outcomes. One important metric for gauging the efficacy of an engineering English translation application is the accuracy of the corresponding deep learning model’s translation. This research presents a deep learning-based solution for automatic mistake identification in machine translation results as a means of closing this gap. In this research, we propose a deep generative model to synthesis the training data that will be utilized to train a model to correct grammatical errors in English used in international trade. Next, the learner’s corpus is corrected using the grammatical fault alteration approach, and “error-correct” sentence pairs are created using the exacted aim sentences and by hand interpreted standard sentences and fed back to the error generation technique for retraining. The model’s error detection and correction capabilities are enhanced through the establishment of a connection amid the grammatical error recognition and correction model. The suggested error recognition method has been shown to considerably increase the stealthiest of the trigger, guarantee the efficacy of the backdoor assault, and make the trigger resistant to specific data augmentation operations in experiments using datasets like GTRSB.

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microgrids with day-ahead energy forecasting for efficient energy management in smart grids: hybrid CS-RERNN

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Australian Journal of Electrical and Electronics Engineering 2024年第3期21卷 213-227页

作者： Shirley, C.P. Pattar, Jagannath Kavitha Rani, P. Saini, Sumit Ranga, Jarabala Elangovan, D. Venkatakrishna Reddy, Ch. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Mechanical Engineering Madanapalle Institute of Technology & Science Madanapalle India Department of M.Tech Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Electrical Engineering School of Engineering & Technology Central University of Haryana Mahendergarh India Detartment of Electrical and Electronics Engineering Ramachandra College of Engineering Eluru India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Electrical and Electronics Engineering Chaitanya Bharathi institute of technology Hyderabad India

By integrating smart grid technology with home energy management systems, households can monitor and optimise their energy consumption. This allows for more efficient use of energy resources, reducing waste and lowering energy bills. In this manuscript, a hybrid approach is proposed for smart grid home energy management with microgrids and day-ahead energy forecasts. The proposed control approach combines the Circle Search (CS) algorithm and Recalling-Enhanced Recurrent Neural Network (RERNN). Commonly it is named as CS-RERNN technique. The novelty of this paper is to optimise energy consumption and production within microgrids, thereby contributing to the overall efficiency of the smart grid system. The proposed method is used to reduce the electricity cost, peak-to-average ratio (PAR), and maximising consumer comfort. Energy management is performed based on the CS algorithm. A smart home connected to the external power grid (PG) is managed by the proposed method. Here, load demand is predicted by using RERNN. By then, the performance of the proposed method is implemented in mATLAB platform. The proposed method shows a high efficiency of 96%, and 22 $ of low electricity bill cost compared with other existing methods such as Particle swarm optimisation (PSO), Cuckoo search algorithm (CSA, and Border collie Optimisation (BCO). ©, Engineers Australia.

关键词： Energy utilization

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A Survey on Securing Payload in mQTT and a Proposed Ultra-lightweight Cryptography 4th

A Survey on Securing Payload in MQTT and a Proposed Ultra-li...

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4th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2020

作者： Nwiah, Edward Kant, Shri M. Tech CSE—Networking & Cybersecurity Department of Computer Science Sharda University Knowledge Park III Greater NoidaUttar Pradesh201310 India Department of Computer Science Sharda University Knowledge Park III Greater NoidaUttar Pradesh201310 India

ISBN: (纸本)9789811570612

Internet of things foresees devices that are connected globally, accessing and sharing pertinent information to make life easier and better. This highly interconnected global network seeks to advance business productivity, government efficiency and agriculture growth. However, these fantastic opportunities also present a number of significant security challenges. Quite a number of researches have gone into the domain of Internet of things (IoT). mQTT which is the short form for message Queuing Telemetry Transport is an application layer protocol that has been proposed to smoothen publish and subscribe procedures in exchanging data between client and the server. mQTT, however, was designed without proper security imbibition. In this paper, recent security mechanisms for enhancing the security of the mQTT protocol have been studied and analysed comparatively. Based on the challenges that surfaced from the survey, this research proposes an ultra-lightweight cryptography, Hummingbird-2 as a novel security solution. This is done to achieve bandwidth and memory efficiency, quality of service of data delivery. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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Enhanced Prediction of Fuel Consumption and Carbon Emissions in Light-Duty Vehicles using Convolutional Recurrent Imputation Network

Enhanced Prediction of Fuel Consumption and Carbon Emissions...

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International Conference on computer, Electrical & Communication Engineering (ICCECE)

作者： T. Lalitha Jerzy Ryszard Szymański marta Żurek-mortka mithileysh Sathiyanarayanan K. R. Srilekha Computer Science School of Information Science Presidency University Bengaluru Karnataka India Department of Electrical Drives and Industrial Electronics Faculty of Transport Electrical Engineering and Computer Sciences Casimir Pulaski University of Radom Poland Europe Lukasiewicz Research Network Institute for Sustainable Technologies Kazimierza Pułaskiego Radom Poland Europe MIT Square London I M.Tech Integrated(Software Engineering) VIT University Vellore Tamilnadu India

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

Fuel utilization and emission estimation are essential for assessing the consequence of materials and strict emission control measures due to the startling rate of weather changes. The world's socioeconomic issues are significantly impacted by the growing worldwide average temperature and its effects on climate change. The melting of the polar ice caps, that is a direct result of this rise, leads to a number of other problems, such as the polar animal’s death, coastal flooding, and exposure to bacteria and earliest microorganisms that are frozen in the snow and could cause many more global pandemics and invisible illnesses. In this paper, Enhanced Prediction of Fuel Consumption and Carbon Emissions in Light-Duty Vehicles using Dynamic Graph Convolutional Recurrent Imputation Network (EP-FCCE-DGCRIN) are proposed.. The effectiveness of the EP-FCCE-DGCRIN technique is compared to other existing methods. This research offers evidence-based suggestions to lessen the environmental effects of vehicles for both manufacturers and consumers depending upon the outcomes of the Descriptive Statistics analysis.

关键词： Temperature measurement Pandemics Snow Sea measurements Carbon dioxide Imputation Fuels Vehicle dynamics Socioeconomics meteorology

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Intelligent Deep Learning Framework for Breast Cancer Prediction using Feature Ensemble Learning 4

Intelligent Deep Learning Framework for Breast Cancer Predic...

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4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Rao, Chintala Nageswara Chatrapathy, K. Jainul Fathima, A. Sathish, Goski mukherjee, Saptarshi Reddy, Pundru Chandra Shaker CSE-AI&ML Geethanjali College of Engineering and Technology Hyderabad India School of Computing&Information Technology REVA University Karnataka Bengaluru India Information Technology Francis Xavier Engineering College TamilNadu Tirunelveli India Information Technology St. Martins Engineering College Dhullapally Hyderabad India M.Tech-Bioinformatics Maulana Abul Kalam Azad University of Technology Uttarakhand India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350305258

The mortality and morbidity rates associated with breast cancer are extremely high. In the fight against breast cancer (BC), a correct diagnosis is crucial. The diagnostic process can benefit from the application of machine-learning (mL) and deep-learning (DL) strategies. In this paper, a deep-recurrent-neural-network (DRNN) technique optimized for breast cancer analysis using RNN and the Keras-Tuner optimization technique is proposed. The input layer, five hidden layers, five dropout layers, and the output layer make up the optimized DRNN. It fine-tuned the dropout layer's neurologist count and firing rate for each successive hidden layer. The most crucial data points have been elected utilizing three different feature-selection methods. The optimized DRNN was contrasted to five standard mL strategies: decision-tree (DT), support-vector-machine (SVm), random-forest (RF), Naïve-Bayes (NB), and the K-nearest neighbor algorithm (KNN). The elected features have been applied to both standard mL models and optimized DRNN. The outcomes showed that, compared to the other models, the cross-validation (CV) and testing outcomes were best achieved (99.16%) by the optimized DRNN with the features picked by univariate. © 2023 IEEE.

关键词： Breast cancer Carcinoma Deep Learning KNN machine learning RNN SVm

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Brain Tumor Classification Using UNet Deep Neural Networks from 3D mRI Images

Brain Tumor Classification Using UNet Deep Neural Networks f...

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

作者： mohammad Khaja Shaik D Kalpana U Sesadri Saptarshi mukherjee C Dastagiraiah Pundru Chandra Shaker Reddy Electronics and Communication Engineering St. Ann's College of Engineering and Technology Chirala Andhra Pradesh India Departmet of CSE-Data Science Malla Reddy Engineering College (Autonomous) India Computer Science and Engineering Vardhaman College of Engineering Hyderabad Telanagana India M.Tech-Bioinformatics Maulana Abul Kalam Azad University of Technology West Bengal India Computer Science and Engineering Anurag University Hyderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371819

A patient's therapy options and prognosis are both improved by detecting and diagnosing a brain tumor early. Brain tumors can be located and diagnosed with the help of magnetic resonance imaging (mRI). In real practice, however, medical personnel rely entirely on their own time and expertise when manually identifying brain tumors from a vast number of mRI scans. These days, medical diagnosis and treatment recommendation systems that use computer-aided expertise are all the rage. Brain tumor detection makes use of a plethora of frameworks based on machine-learning(mL) &deep-learning(DL). The purpose of this research is to present a DL-based approach for effective brain tumor segmentation and classification. The research uses convolutional-neural-networks(CNNs) for tumor classification after volumetric segmentation of mRI-images using the 3D-UNet model. To demonstrate the models' validity, loss and precision plots are provided. We compare the outcomes of several methods described in the literature with the measured performance of the models we offer. When compared to the most recent cutting-edge methods, the proposed work comes out on top.

关键词： Training Image segmentation Solid modeling Three-dimensional displays magnetic resonance imaging Computational modeling Artificial neural networks

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monitoring state of mind using Natural Language Processing

Monitoring state of mind using Natural Language Processing

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International Conference on Advances in Computing, Communication and Control (ICAC3)

作者： Vemula Sai Saketh Srinivas Khatravath Yashvanth Kumar Guntupalli B.Tech Computer Science PES University Bangalore India M.Tech Electronics and Electrical Communication IIT Kharagpur Kharagpur India

ISBN: (纸本)9781665426350

State of mind is essential in every person’s life to keep their life stable, calm and peaceful. The key factors that contribute towards them are mood, emotion and thoughts. monitoring them is key to a person's overall development and allows family and friends to support them to a greater extent. One aspect of getting a report on this is using the words being typed by the person on their computer or mobile, browser history, songs and movies played. This paper proposes a sentiment analysis system where information about mood and thoughts are conveyed by means of natural language processing. These logs captured during each time session are classified to gain insights about the person. This reduces the likelihood of any drastic decisions taken by the person and provides a better means of conveying the information and maintaining communication between them and their relations.

关键词： Sentiment analysis mood motion pictures Browsers History monitoring

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