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IoT based Control Networks and Intelligent systems (ICICNIs), International Conference on

作者： R Deepika P. shanmugam K. Moorthi P. K. Manoj Kumar s. L. swarna Ramnath. K Department of Artificial Intelligence and Data Science B V Raju Institute of Technology Narsapur Telangana India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Electronics and Communication Engineering Sri Sairam Engineering College Chennai India School of Computational Intelligence Joy University Tirunelveli India Department of Computer Science and Engineering Sona college of Technology Salem Tamilnadu India Department of Computer Science and Engineering K.L.N College of Information Technology Madurai Tamilnadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Lung cancer has become one of the primary causes of death worldwide due to cancer, which indicates the necessity of early diagnosis. In this regard, the present work proposes an optimized model of transfer learning based on characterizing different stages of lung cancer by using computed tomography images acquired from the IQ-OTH/NCCD lung cancer database downloadable from Kaggle. It makes use of a two-step process: first, a U-Net model is used on the patient's CT images to reconstruct anatomical features and reduce noise; second, a shuffleNet architecture is used to predict the patient's CT stage using the reconstructed image. In addition, to enhance classification, PsO was incorporated into the model so that it can optimize the hyperparameters. The accuracy of the model was at 97.85 percent, and it suggests that the model can become a very useful tool for clinicians to bring about earliest identification and staging for lung cancer.

关键词： Accuracy Databases Computational modeling Computed tomography Transfer learning Noise Lung cancer Predictive models Image reconstruction Optimization

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Time-series Cryptocurrency Forecasting Using Ensemble Deep Learning

Time-Series Cryptocurrency Forecasting Using Ensemble Deep L...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： K Rama Rao M Lakshmi Prasad G Ravi Kumar R Natchadalingam Mohammad Manzoor Hussain Pundru Chandra shaker Reddy Electronics and Communication Engineering Geethanjali College of Engineering and Technology Hyderabad Telangana India Computer Science and Engineering Institute of Aeronautical Engineering Dundigal Telangana India Computer Science and Engineering CMR College of Engineering & Technology Hyderabad India Computer Science and Engineering SRM Madurai College for Engineering and Technology Madurai Tamilnadu India Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India

Cryptocurrency is now widely accepted as a payment and exchange method, permeating nearly every aspect of the financial sector. similar to the non-stationary and very erratic price movements of traditional stocks, cryptocurrency price swings are highly unpredictable. The rising popularity of cryptocurrencies has prompted an increase in the number of studies conducted to predict their future prices. The importance of cryptocurrency forecasting has grown significantly with the advent of deep learning. portfolio optimisation and decision making cannot be achieved without the creation of a smart forecasting model. The primary contribution of this study is the utilize of traditional deep learning (DL) models in combination with the three most popular ensemble learning algorithms (ensemble-averaging, bagging, and stacking) to predict the hourly values of major cryptocurrencies. Traditional DL strategies consisting of combinations of long short-term memory (LsTM), bi-directional (biLsTM), and convolutional layers were utilized to assess the suggested ensemble methods. The ensemble techniques were tested on their capacity to forecast the price of a cryptocurrency an hour basis (regression) and to determine whether the price will rise or fall relative to the present (classification). Our in-depth experimental research shows that combining ensemble learning with deep learning can produce robust, stable, and trustworthy forecasting strategies.

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A Novel Design of an Image Encryption and Decryption scheme Using Enhanced Cybersecurity Principles 1

A Novel Design of an Image Encryption and Decryption Scheme ...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCs 2023

作者： Ganesamoorthy, R. Pushpa, G. senthilkumar, b. Krishnaprasanna, R. vijayalakshmi, G. Pandi, v. samuthira Panimalar Engineering College Department of Ece Tamil Nadu Chennai India E.G.S.Pillay Engineering College Tamil Nadu Nagapattinam India V.S.B. Engineering College Department of Eee Tamil Nadu Karur India Sathyabama Institute of Science and Technology Department of Ece Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Ece Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350359756

Information security has risen to the top of the priority list as a direct result of the fast expansion of digital communication transmission and delivery. Protecting sensitive picture information is becoming more important than it has ever been before as the usage of images becomes increasingly common. The requirement to protect one's reputation has reached the level of an absolute necessity. The safeguarding of one's privacy has developed into an activity of critical importance. There is a wide range of different approaches that have been developed as a means of protecting sensitive data as well as the privacy of individuals. Images are encrypted to protect sensitive data from being viewed by unauthorized parties. Encryption is a popular practice that is utilized for the purpose of protecting confidential information from prying eyes. In this study, we enhance the performance of image encryption by utilizing a revised version of the Enhanced Image Cryptography Principle (EICP) that makes use of a key stream generator. This helps us get better results. All cryptographic approaches for sending pictures over an unsecured network make use of image cryptography approaches. The researchers have developed a number of such methods for safely and effectively communicating information over an unsafe network and these approaches are used by all cryptographic approaches. This study aims to demonstrate the restricted encryption methods that were applied to disguise the image over a susceptible network in order to accomplish its purpose. This research proposes a novel encryption method that has been given the acronym EICP. The goal of this method is to improve image cryptography in a timely manner. The proposed approach is cross-validated with the conventional cryptographic algorithm called Rivest, shamir, and Adleman (RsA) to prove the efficiency of the proposed approach in fine manner. © 2023 IEEE.

关键词： Cybersecurity

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Geospatial Data Exploration Using Machine Learning

Geospatial Data Exploration Using Machine Learning

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smart Electronics and Communication (ICOsEC), International Conference on

作者： K. Geetha A. srivani s. Gunasekaran s. Ananthi s. sangeetha Department of Information Technology MAM College of Engineering and Technology Trichy Tamil Nadu India Department of computer science and engineering Mahendra collegeo of engineering Tamil Nadu India Department of Computer Science and Engineering V.S.B. Engineering college Karur Tamil Nadu India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Information Technology Kongunadu College of Engineering and Technology Trichy Tamil Nadu India

Geospatial data exploration's major objective is to learn more about spatial characteristics in order to decide accommodation of students in various locations. The K-means technique, which is frequently used in geospatial analysis to find spatial patterns or hotspots of particular phenomena, is the algorithm utilised in this research. It is used to organise data points into clusters based their similarity. Urban planning, transportation analysis, and epidemiological research, among other subjects, can all benefit greatly from this kind of analysis. The process of looking into and displaying patterns, trends, and relationships within data that has been gathered and documented using geographic information is known as exploratory analysis of geolocational data. It will be possible to accommodate in this study.

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An Improvised Method for Anomaly Detection in social media using Deep Learning

An Improvised Method for Anomaly Detection in social media u...

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2022 International Conference on Electronics and Renewable systems, ICEARs 2022

作者： sivakumar, C. sathyanarayanan, D. Karthikeyan, P. velliangiri, s. Sree Vidyanikethan Engineering College Department of Computer Science and Systems Engineering Andhra Pradesh India Jain University Dept of Cse Bangalore India B v Raju Institute of Technology Dept of Cse Telangana Narasapur India

ISBN: (纸本)9781665484251

Recently, social media has arisen not only as a personal communication media, but also, as a media to communicate opinions about products and services or even political and general events among its users. Due to its widespread and popularity, there is a massive amount of user reviews or opinions produced and shared daily. Twitter is one of the most widely used social media micro blogging sites. In this paper, a deep learning-based approach is developed to detect the anomalies in social media using text mining. The emotional classification is considered as a part of the model that classifies emotional anomalies present in the text. Classification of such text is conducted via proper training and testing of the classifier. © 2022 IEEE.

关键词： Anomaly detection

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Implementation and Prediction of Accurate Data Forecasting Detection with Different Approaches

Implementation and Prediction of Accurate Data Forecasting D...

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smart Electronics and Communication (ICOsEC), International Conference on

作者： b. Uma Maheswari s. Kirubakaran P. saravanan M. Jeyalaxmi Alabazar Ramesh R G vidhya Department of Computer Science and Engineering St. Joseph's College of Engineering Chennai Department of Computer Science Engineering CMR College of Engineering and Technology Kandlakoya Telangana Department of Electronics and Communication Engineering Sri Sairam institute of technology Chennai 44 Physics Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India Department of ECE HKBK College of Engineering Bangalore

The ability to recognize anomalies and make accurate forecasts is essential to the practice of many disciplines, including finance, economics, meteorology, and anomaly detection. The installation and prediction of reliable forecasting detection using various methodologies are the primary objectives of this abstract's discussion. The accuracy of forecasting and detection models has been improved through the development of a variety of strategies, methodologies, and approaches. Among these are the more common statistical methods, algorithmic approaches to machine learning, and more recent deep learning methods. Each method has advantages and disadvantages, and the degree to which it is successful is highly dependent on the nature of the data as well as the specific application area. In order to put accurate forecasting detection into action, the data must first be preprocessed, then appropriate features must be identified, and finally, models must be trained utilizing the method that was selected. After then, the models are judged according to how well they can forecast the outcomes of future occurrences or identify deviations from the norm. The purpose of this abstract is to investigate and evaluate the effectiveness of several methodologies regarding accurate forecasting detection. This article addresses the benefits and drawbacks of each technique, as well as the difficulties associated with putting them into practice and their capacity to make accurate forecasts. In addition, the abstract investigates how the accuracy of predicted detection is affected by a variety of aspects including data quality, model complexity, feature selection, and training approaches. This demonstrates the significance of utilizing dependable metrics of evaluation and processes of validation in order to produce accurate and trustworthy predictions. The facts and results that are discussed in this abstract help to a greater knowledge of the many methods that can be utilized for accurate fo

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Gesture Recognition Machine vision video Calling Application Using YOLOv8

Gesture Recognition Machine Vision Video Calling Application...

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International symposium on Communications and Information Technologies (IsCIT)

作者： Pruthvi Darshan s s shantakumar b Patil bhargav s Patil Premjyoti Department of Computer Science & Engineering Sai Vidya Institute of Technology Bengaluru Karnataka India Department of Computer Science & Engineering B.M.S. College of Engineering Bengaluru Karnataka India

Gesture recognition pertains to a computer system’s capacity to identify and understand hand gestures, categorizing them into captions or text. This technology holds vast potential for applications in education, communication, and accessibility, especially for individuals with hearing disabilities. This research introduces a gesture recognition video calling application, enabling communication among individuals with and without speech and hearing abilities. To enhance accuracy, the application employs Darknet’s YOLOv8 for object detection. YOLOv8 is a robust and efficient system that excels in various computer vision tasks, such as object detection, instance segmentation, and image classification. The paper proposes a trained model for image classification, encompassing ten different hand gesture classes, which processes each video frame during the call, achieving a remarkable 98% accuracy with a low latency of 10ms. The application displays the classification results on top of the interface during the video call.

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Mechanisms of reproductive isolation between annual and perennial plants of Incarvillea sinensis

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植物分类学报 2024年第3期62卷 548-560页

作者： Wen-Juan Lan Fang-Yuan Wang spencer C.H.barrett Wen-Ting Wang Yue Ma Yang Yang Nan Li Jun-Chen Deng Wei-Ning bai Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering College of Life SciencesBeijing Normal UniversityBeijing 100875China Department of Ecology and Evolutionary Biology University of TorontoToronto M5S 3B2ONCanada School of Mathematics and Computer Science Northwest Minzu UniversityLanzhou 730030China Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering College of Life SciencesBeijing Normal UniversityBeijing 100875China Institute of Environmental Sciences Faculty of BiologyJagiellonian UniversityGronostajowa 7Kraków 30-387Poland Doctoral School of Exact and Natural Sciences Jagiellonian UniversityGronostajowa 7Kraków 30-387Poland

Quantifying the relative strength of isolating barriers is a major focus of research on plant *** life histories and mating systems have the potential to limit gene exchange between closely related populations growing in ***,few studies have quantified reproductive isolating barriers between conspecific annual and perennial populations and their contributions to total reproductive isolation(RI).Incarvillea sinensis Lam.(bignoniaceae)is an insect-pollinated herb with largely allopatric annual and perennial populations that differ in mating *** perennial populations are primarily outcrossing whereas annual populations are predominantly *** a rare sympatric site in northern China we estimated prezygotic and postzygotic barriers to gene exchange between annual and perennial plants and found complete Rl between the two life *** exhibited significantly higher ecogeographic isolation than perennials whereas perennials experienced more isolation through pollen-pistil interactions than *** between annuals and perennials demonstrated that postzygotic barriers influencing fruit and seed formation,F1 germination and survival were negligible for annuals but played a small role for ***,F,hybrids of crosses between annuals and perennials produced no pollen and their ovules were largely *** study provides insight into the relative importance of prezygotic and postzygotic isolating barriers between closely related annual and perennial populations of *** and some of these barriers could have been involved with *** and perennials of *** represent two biological species and thus deserve to be recognized as distinct taxonomic species.

关键词： asymmetric barriers to gene exchange Incarvillea sinensis life history mating system reproductive isolation

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Optimization Model for Energy Management in a Microgrid 2

Optimization Model for Energy Management in a Microgrid

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： satish, G. Dalai, Chitaranjan Dattu, vinjamuri s. N. Ch. Rayudu, K. sathish, T. Purohit, Kamlesh Chandra Sree Dattha Institute of Engineering and Science Department of Electrical and Electronics Engineering Telangana 501510 India Odisha University Technology and Research Department of Civil Engineering Odisha Bhubaneswar751029 India Aditya Engineering College Department Of: Mechanical Engineering Andhra Pradesh Surampalem533437 India B. V. Raju Institute of Technology Department of Electrical and Electronics Engineering Telangana Narsapur502313 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9781665456302

This article presents an optimization model for energy management (EM) in a microgrid composed of renewable energy sources. The microgrid in question, corresponding to an electric vehicle (Evs) charging station, has solar panels, wind turbines, and a bank of batteries. The loads considered are Evs that enter the charging station requesting the charging of their batteries and equipment from a small convenience store, located in the charging station itself. This work maximize the supply of active power to Evs and, simultaneously, minimize the interruptions in the energy supply. An algorithm (with the aid of computational tools) will be used to deal with the decision-making process related to turbines, panels and loads. The implementations were made in MATLAb, for modeling the energy sources, and AMPL, for applying the optimization algorithm. © 2023 IEEE.

关键词： Renewable energy resources

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Analysis of Protein-Ligand Interactions of sARs-Cov-2 Against selective Drug Using Deep Neural Networks

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big Data Mining and Analytics 2021年第2期4卷 76-83页

作者： Natarajan Yuvaraj Kannan srihari selvaraj Chandragandhi Rajan Arshath Raja Gaurav Dhiman Amandeep Kaur St.Peter’s Institute of Higher Education and Research Information Communication Technology AcademyChennai 600096India Department of Computer Science and Engineering SNS College of Engineering641107India Department of Computer Science and Engineering Jagannath Educational Health and Charitable Trust College of Engineering and TechnologyCoimbatore 641105India B.S.Abdur Rahman Crescent Institute of Science and Technology Chennai 600048India Department of Computer Science Government Bikram College of CommercePatiala 147001India Department of Computer Science and Engineering Sri Guru Granth Sahib World UniversityFatehgarh 140406India

In recent time, data analysis using machine learning accelerates optimized solutions on clinical healthcare systems. The machine learning methods greatly offer an efficient prediction ability in diagnosis system alternative with the clinicians. Most of the systems operate on the extracted features from the patients and most of the predicted cases are accurate. However, in recent time, the prevalence of COvID-19 has emerged the global healthcare industry to find a new drug that suppresses the pandemic outbreak. In this paper, we design a Deep Neural Network(DNN)model that accurately finds the protein-ligand interactions with the drug used. The DNN senses the response of protein-ligand interactions for a specific drug and identifies which drug makes the interaction that combats effectively the virus. With limited genome sequence of Indian patients submitted to the GIsAID database, we find that the DNN system is effective in identifying the protein-ligand interactions for a specific drug.

关键词： Deep Neural Network(DNN) coronavirus protein-ligand interactions deep learning clinical healthcare system

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