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High-dimensional nonlinear variable separation solutions and novel wave excitations for the(4+1)-dimensional Boiti-Leon-Manna-Pempinelli equation

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Communications in Theoretical Physics 2024年第11期76卷 1-11页

作者： Zu-feng Liang Xiao-yan Tang Wei Ding Department of Physics Hangzhou Normal UniversityHangzhou 310036China School of Mathematical Sciences Key Laboratory of Mathematics and Engineering Applications(Ministry of Education)&Shanghai Key Laboratory of PMMPEast China Normal UniversityShanghai 200241China School of Computer Science and Technology Shanghai Normal UniversityShanghai 200234China

Considering the importance of higher-dimensional equations that are widely applied to real nonlinear problems,many(4+1)-dimensional integrable systems have been established by uplifting the dimensions of their corresponding lower-dimensional integrable ***,an integrable(4+1)-dimensional extension of the Boiti-Leon-Manna-Pempinelli(4DBLMP)equation has been proposed,which can also be considered as an extension of the famous Korteweg-de Vries equation that is applicable in fluids,plasma physics and so *** is shown that new higher-dimensional variable separation solutions with several arbitrary lowerdimensional functions can also be obtained using the multilinear variable separation approach for the 4DBLMP *** addition,by taking advantage of the explicit expressions of the new solutions,versatile(4+1)-dimensional nonlinear wave excitations can be *** an illustration,periodic breathing lumps,multi-dromion-ring-type instantons,and hybrid waves on a doubly periodic wave background are discovered to reveal abundant nonlinear structures and dynamics in higher dimensions.

关键词： (4+1)-dimensional Boiti-Leon-Manna-Pempinelli equation variable separation solution periodic breathing lumps multi-dromion-ring-type instanton hybrid waves on a doubly periodic wave background

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Integrating Random Forest and Support Vector Regression Models for Optimized Energy Consumption Evaluation in Cloud Computing Data Centers 3

Integrating Random Forest and Support Vector Regression Mode...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Shaik, Nazeer Kumar, B. Vinoth Bhati, Anita Kumar, Sandeep Singh, Jagendra Sahu, Dinesh Prasad SRIT-Autonomous Department of Computer Science Engineering Anantapur India Ksr Institute for Engineering and Technology Dept. of Electronics and Communication Engineering Tiruchengode India Jecrc University Department of Computer Applications Jaipur India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350342338

This study proposes a novel approach for energy consumption evaluation in cloud computing data centers. The immense energy consumption of these data centers presents significant environmental and economic challenges. Traditional evaluation methods often fail to provide accurate estimations due to the complex dynamics of cloud computing environments. The research introduces an integrated model, combining Random Forest Regression and Support Vector Regression, along with a Gradient Boosting Regressor for iterative training of regression parameters. We measure the model's performance using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), replacing traditional relative deviation measures. The proposed approach aims to provide more accurate and efficient energy consumption evaluations, offering potential benefits in energy management, operational costs reduction, and minimizing environmental impact. © 2023 IEEE.

关键词： Cloud Computing Energy Consumption Evaluation Gradient Boosting Regressor Random Forest Regression Support Vector Regression

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Multidimensional binary layered model for census prediction using machine learning techniques

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Asian Journal of Civil engineering 2024年第6期25卷 4877-4891页

作者： El-Salhi, Subhieh Albqowr, Hala Mahmoud Saleh Igried, Bashar Awwad, Sari Dittakan, Kwankamon Department of Computer Information System The Hashemite University Zarqa 13133 Jordan Surveying and Geomatics Engineering Department Al-Balqa Applied University Al-Salt 19117 Jordan Department of Computer Science and Its Applications The Hashemite University Zarqa 13133 Jordan College of Computing Prince of Songkla University Phuket 83120 Thailand

The development of the resource allocation process is based on essential factors, such as population size, which can be considered one of the most critical ones. Estimating the population size for a particular region, especially in developing countries, indicates the government’s authority to invest and utilize the resources efficiently. As a result, forecasting population size is one of the most essential requirements that guide the entire development process and, as a result, affects human well-being. This paper presents a novel technique for census seven prediction of a specific region of interest in the context of satellite images. The Multidimensional Binary Layered Model (MDBL) is a representation technique founded on identifying a given region of interest in terms of the geographical nature of the local surrounding regions. The MDBL is then employed to generate an "effective" classifier using different machine learning techniques such as the Convolutional Neural Network (CNN), k-nearest Neighbor (k-NN), and Support Vector Machine (SVM). The Black Widow Optimization (BWO) method has been used to maximize performance and provide the best machine learning model. In order to produce the best prediction model, this optimization method ensures the models are tuned with the most appropriate hyperparameters. To this end, Al-Karak City, in Jordan, has been used as a case study to evaluate the proposed model. The evaluation of the classifiers has been reported in terms of the accuracy and the Area Under Curve (AUC) measurements. The CNN achieved the “best” overall AUC result of 88%, indicating the significant impact of using the proposed MDBL to generate an accurate, effective classifier. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Binary layered model Black widow optimization (BWO) Census prediction Classification CNN Machine learning

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A Framework for the prediction of Diabtetes Mellitus using Hyper-Parameter tuned XGBoost Classifier 13

A Framework for the prediction of Diabtetes Mellitus using H...

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13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Gayathri, R. Pati, Peeta Basa Singh, Tripty Nair, Rekha R Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Computer Science and Engineering Bangalore India Dayananda Sagar University School of Engineering Department of Computer Applications Bangalore India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Diabetes Mellitus is caused by the increase of blood glucose level in the body. It is likely to be the most common disease for the human community which is about 8.8% of the entire population. It is important to identify this disease in the initial stages of the development to reduce the risk factor of further heart diseases and other vital problems. Machine Learning is the one of the recent developments which is very helpful in clinical care guidelines. The PIMA Indian Diabetes dataset is used to predict Type-2 Diabetes Mellitus based on certain clinical diagnostic measurements for females. In this paper, we proposed a framework for prediction of Diabetes Mellitus using Optimised Gradient Descent Boosting Classifier. The performance metrices such as Accuracy, Sensitivity, Specificity and F1 scores are chosen. These experiments are conducted for PIMA Indian Diabetes Dataset and the proposed classifier yields 94.5 %, 92.4 %, 96 % and 92 % for the values in Accuracy, Sensitivity, specificity and F1 score and also compared with other classifier's like K-NN, QDA, SVM and etc. © 2022 IEEE.

关键词： Forecasting

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A Review on Collision Avoidance Application in Automotive Ad Hoc Networks Employing a Novel Source-Based Ant Colony Optimization Routing Protocol Using a Cross-Layer Approach

A Review on Collision Avoidance Application in Automotive Ad...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Lekshmi, V. Pramila, R Suji Symon, Tibbie Pon Noorul Islam Centre for Higher Education Department of Computer Applications Tamil Nadu Thackalay India Mar Baselios Institute of Technology and Science Department of Computer Science and Engineering Kerala India Noorul Islam Centre for Higher Education Department of Electrical and Electronics Engineering Tamil Nadu Thackalay India

ISBN: (纸本)9798350333244

The term 'Vehicle Ad hoc Network' (VANET) refers to a system in which a collection of non-stationary or stationary vehicles share a wireless connection. Until recently, VANETs were primarily used to improve the safety and convenience of driving. Both the AntHocNet method and the Ant based Routing Algorithm (ARA) rely on ACO. It is a combinational method that combines proactive route maintenance with a reactive setup process for route finding. Its objective is to preserve information about current routes and identify new ones. The term 'Ant Colony Optimization' (ACO) refers to a population-based mathematical heuristic that's capable of providing approximate solutions to difficult optimization problems. The aim of ACO is to solve an optimization problem by sending out a colony of virtual ants to find an optimal solution. Ant colony optimization is a probabilistic technique for finding optimum paths. In this paper a detailed literature review for VANET for collision avoidance application based on Ant colony optimization is surveyed. © 2023 IEEE.

关键词： Ant colony optimization

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Deep Neural Network Deception Using A Single Pixel Assault 6

Deep Neural Network Deception Using A Single Pixel Assault

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Singh, Digvijay Yadav, Annu Arora, Rishabh Ravivarman, G. Vijay, S. Arul Antran Sharma, Y.K. School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand India School of Computer Science and Applications IIMT University Meerut India K. R. Mangalam University Department of Civil Engineering Gurugram India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Karpagam College of Engineering Department of Computer Science Engineering Coimbatore India Computer Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350304480

Previous studies have shown that Deep Neural Networks'(DNN's) outputs is not consistent and very susceptible to inputs vectors perturbations of any size, and as a result, a number of techniques have been developed for creating effective perturbations on the connections. In this research, we offer a novel divergent transformation method for visually computing very small adversarial perturbations (few-pixel attacks). It works with a wider range of DNN different classifiers and calls for far less adverse data. The findings indicate that, on average, 73.8percent of total of the test photos can be modified to create hostile images by changing just one pixel. Additionally, it is well recognized that analyzing the DNN resilience problem can provide crucial hints for comprehending the geometric characteristics of the DNN decision. a high-dimensional inputs area map. In contrast to earlier research, the outcomes of performing few-pixel attack give quantifiable data and analyses to the geometric knowledge, Genetic algorithm (GA) © 2023 IEEE.

关键词： DNN geometric hostile images Pixel

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Secure and Efficient Data Transmission for IoT Devices in Untrusted Environments

Secure and Efficient Data Transmission for IoT Devices in Un...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Jain, Upma Smitha, K. Battula, R.S.S. Raju Sarkar, Arindam Martin, Natasha Natrayan, L. Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Adichunchanagiri University Bgs Institute of Technology Department of Artificial Intelligence and Machine Learning India Aditya University Department of Computer Applications India Heritage Institute of Technology Department of Applied Electronics and Instrumentation Engineering Kolkata India Department of Law Symbiosis Law Pune India School of Engineering Simats Department of Mechanical Engineering Saveetha India

ISBN: (纸本)9798350375442

One technological trend that will not go away in the modern world is the Internet of Things (IoT). With the development of the Internet of Things, the world is becoming smarter. The efficacy, flexibility, and simplicity of deployment of IoT make it the preferred choice for most real-time applications. But energy efficiency and security are the big obstacles for the Internet of Things. Presented here are safe, energy-efficient solutions for the Internet of Things (IoT), a problem whose significance this study acknowledges. Since routing is the primary energy consumer, this study presents secure routing strategies that are both energy efficient and reliable. Three distinct safe data transmission techniques for the Internet of Things (IoT) are presented in this paper, all of which aim to reduce energy usage during routing. The first part of this study lays out a clustering-and secure route selection-based architecture for safe and energy-efficient data transmission with the Internet of Things (IoT). The W-HHO technique, which is based on Whale Optimization, is used to identify the secure routes, and fuzzy-based secure clustering is used for that purpose. Encryption protocols that work in tandem with cypher block chaining ensure the safety of sensitive information. Using industry-standard metrics like packet delivery rate, throughput, energy efficiency, network longevity, etc., we assess how well each solution performs. When compared to the current methods, the outcomes produced by the presented works demonstrate promising performance. © 2024 IEEE.

关键词： clustering data transmission energy efficiency fuzzy clustering harris hawks optimization (HHO) internet of things (IoT) secure routing security whale optimization algorithm

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Advancements in Crown Sheath Rot Detection using DenseNet121 and UNet in Rice Plant Leaf Analysis

Advancements in Crown Sheath Rot Detection using DenseNet121...

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Thilagavathy, R. Kumar, S.Senthil Deebalakshmi, R. Deepa, D. Kavitha, R. Kumar, L. Sathish Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur India School of Arts and Science Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Tiruchirappalli India Kongu Engineering College Department of Computer Science and Engineering Tamil Nadu Perundurai India Karpagam Academy of Higher Education Department of Computer Applications TN Coimbatore India Vit Bhopal University School of Computing Science and Engineering MP Kotrikalan India

ISBN: (纸本)9798350348910

This article aims to investigate the leaf spot disease in rice, a condition that has been observed to cause substantial reductions in crop yield, ranging from 10% to 50%. The disease is characterized by the appearance of circular lesions on the leaves of rice plants, which gradually expand and cover the entire leaf surface, leading to the eventual death of the affected leaves. In order to achieve automation in the detection and severity classification of the disease under investigation, a range of deep learning models were utilized. These models encompassed Faster DenseNet121 and UNet. These methods proposed in this study was trained and tested using a dataset consisting of 1120 images. The findings of the study indicate that the DenseNet121 approach that yielded the highest level of success demonstrated a noteworthy classification accuracy rate of 95.23%. The results obtained from the integration of image processing techniques with deep learning models demonstrated superior performance compared to the analysis solely based on deep learning models. The integration described in this study presents a novel approach to enhance the efficiency of diagnosing leaf spot disease on rice. By leveraging advanced technology, this integration aims to reduce the time required for diagnosis, minimize the occurrence of human errors, and facilitate the timely application of suitable pesticides to mitigate the detrimental effects of the disease. © 2023 IEEE.

关键词： Integration

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A CNN-Based Framework for Detection and Recognition of Objects Using Transfer Learning: A Case Study on Flower Recognition 1

A CNN-Based Framework for Detection and Recognition of Objec...

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2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Tiwari, Shivam Sharma, Vikrant Rathore, Yogesh Singh Gandhi, Parul Srivastava, Durgesh Sharda University Department of Computer Science & Engineering Greater Noida India Graphic Era Hill University Computer Science and Engineering Dehradun India Sharda University Computer Science and Application UP Greater Noida India Manav Rachna International Institute of Research and Studies Department of Computer Applications Haryana Faridabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350338423

The vast variety of floral species present on Earth poses a considerable challenge in memorizing their individual names. Acquiring knowledge about specific flower types inhabiting diverse environments like forests, mountains, and dense regions becomes crucial for their recognition and differentiation. Consequently, the development of a technique for accurately determining flower categories becomes imperative. The task of distinguishing and categorizing a particular blossom amidst the extensive array of floral species presents a formidable task. Hence, the automation of flower species identification has been a subject of extensive research interest. This research domain is characterized by the utilization of features extracted from floral images in conjunction with specialized algorithms aimed at classifying flower species. A prominent aspect of these endeavors is the employment of three widely recognized flower image datasets. The present study constitutes a comprehensive survey of various methodologies proposed by diverse researchers with the objective of accomplishing precise identification of flower species. © 2023 IEEE.

关键词： Feature extraction

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Named Entity Recognition for Telugu and Kannada on Naamapadam Dataset Using Various Machine Learning and Deep Learning Algorithms

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Procedia computer science 2025年 258卷 3381-3392页

作者： Kuruva Divya Sree Gunti Geethika Aarathi Rajagopalan Nair Deepa Gupta G Veena Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Named Entity Recognition is a crucial task in Natural Language Processing. It involves detecting and categorizing named entities such as individuals, places, and organizations within a text. This process is vital for transforming un-structured text into structured information, which is useful for various applications like information retrieval, question answering, and text summarization. The proposed study investigates bilingual NER for Telugu and Kannada languages using pre-trained language models and embeddings for contextual vectorization. Utilizing the Naamapadam dataset, the research employs IndicFT, MuRIL and IndicBERT embeddings along with machine learning and deep learning algorithms. Notably, utilized only 10% of the considered dataset and achieved results that surpass those reported in the base paper. Among the models tested, MuRIL combined with XGBoost achieved the best performance, with an F1 score of 89.00 for Telugu and 87.00 for Kannada. Performance evaluation demonstrates varying effectiveness across techniques, shedding light on optimal strategies for NER in under resourced languages. These findings contribute to advancing NLP techniques for Indian languages, facilitating broader accessibility and usability in diverse linguistic contexts.

关键词： Natural Language Processing Named Entity Recognition Naamapadam Da-taset Contextual Vectorization Techniques Machine learning models Deep learning models

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