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YARN Schedulers for Hadoop MapReduce Jobs: Design Goals, Issues and Taxonomy

Recent Advances in computer Science and Communications 2023年第6期16卷 44-55页

作者： Kotikam, Gnanendra Selvaraj, Lokesh Department of Information and Communication Engineering Anna University Tamil Nadu Chennai India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore641062 India

Objective: Big Data processing is a demanding task, and several big data processing frameworks have emerged in recent decades. The performance of these frameworks is greatly dependent on resource management models. Methods: YARN is one of such models which acts as a resource management layer and provides computational resources for execution engines (Spark, MapReduce, storm, etc.) through its schedulers. The most important aspect of resource management is job scheduling. Results: In this paper, we first present the design goal of YARN real-life schedulers (FIFO, Capacity, and Fair) for the MapReduce engine. Later, we discuss the scheduling issues of the Hadoop MapReduce cluster. Conclusion: Many efforts have been carried out in the literature to address issues of data locality, heterogeneity, straggling, skew mitigation, stragglers and fairness in Hadoop MapReduce scheduling. Lastly, we present the taxonomy of different scheduling algorithms available in the literature based on some factors like environment, scope, approach, objective and addressed issues. © 2023 Bentham Science Publishers.

关键词： Energy utilization

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Marine Ecosystem Monitoring: Applying Remote Sensing and AI to Track and Predict Coral Reef Health

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Remote Sensing in Earth Systems Sciences 2024年第4期7卷 486-499页

作者： Veeranjaneyulu, Rayavarapu Govindarajan, Dinesh Subramanian, Chandramohan Devi, Deva Uma Banerjee, Sudipta Edpuganti, Sai Krishna Upadhyay, Shrikant Department of Computer Science & amp Engineering PACE Institute of Technology and Sciences Andhra Pradesh Ongole India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur (KTR) Chennai India Department of ECE SCSVMV Deemed University Tamil Nadu Kanchipuram India Department of Computer Science and Engineering Gayatri Vidya Parishad College of Engineering (A) Andhra Pradhesh Visakhapatnam India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Pune Campus Pune India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur Dist. India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India

Coral reefs are essential ecosystems, supporting a diverse range of marine life and offering considerable ecological, economic, and cultural benefits. However, they face increasing threats from climate change, pollution, and other human activities. Thus, effective monitoring and management of coral reefs are crucial for their conservation and sustainability. In this study, we employed a hybrid model, HCNN-SVM, which combines convolutional neural networks (CNNs) for feature extraction and support vector machines (SVMs) for classification. We utilized a coral-reef dataset from Kaggle, containing images from CoralNet and the Moorea Coral Reef Long Term Ecological Research (MCR LTER). To enhance image quality and ensure accurate reflectance values, we applied radiometric, geometric, and water column corrections during preprocessing. Feature extraction involved the use of spectral indices such as the Coral Bleaching Index (CBI) and Normalized Difference Vegetation Index (NDVI) to detect healthy and stressed corals, along with texture analysis to differentiate substrates. The model was trained on this diverse dataset, which captures various environmental conditions and reef types, achieving an accuracy of 98.55%. Cross-validation methods were employed to evaluate model performance, ensuring robustness and generalizability. The HCNN-SVM model demonstrated high accuracy in identifying and mapping coral reef components, making it a powerful tool for monitoring coral reef health and supporting conservation efforts. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks (CNNs) Coral Bleaching Index (CBI) Coral reef monitoring Feature extraction HCNN-SVM Moorea Coral Reef Long Term Ecological Research (MCR LTER) Normalized Difference Vegetation Index (NDVI) Support vector machines (SVMs)

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New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

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computer Modeling in engineering & Sciences 2024年第3期138卷 2749-2767页

作者： Shimaa M.Amer Ashraf A.M.Khalaf Amr H.Hussein Salman A.Alqahtani Mostafa H.Dahshan Hossam M.Kassem Electronics and Communications Engineering Department Higher Institute of Engineering and TechnologyKafr ElsheikhEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMiniaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringTanta UniversityTantaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringHorus University EgyptNew DamiettaEgypt Department of Computer Engineering College of computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia School of Computing Mathematics and EngineeringCharles Sturt UniversityBathurstNSW2795Australia

Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up to ***,it improves the array gain and directivity,increasing the detection range and angular resolution of radar *** study proposes two highly efficient SLL reduction *** techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm(GA)to develop the Conv/GA andDConv/GA,*** convolution process determines the element’s excitations while the GA optimizes the element *** M elements linear antenna array(LAA),the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N=(2M−1).This new vector is divided into three different sets of excitations including the odd excitations,even excitations,and middle excitations of lengths M,M−1,andM,*** the same element spacing as the original LAA is used,it is noticed that the odd and even excitations provide a much lower SLL than that of the LAA but with amuch wider half-power beamwidth(HPBW).While the middle excitations give the same HPBWas the original LAA with a relatively higher *** the increased HPBWof the odd and even excitations,the element spacing is optimized using the ***,the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the ***,for extreme SLL reduction,the DConv/GA is *** this technique,the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation *** provides a relatively wider HPBWthan the original LAA with about quad-fold reduction in the SLL.

关键词： Array synthesis convolution process genetic algorithm(GA) half power beamwidth(HPBW) linear antenna array(LAA) side lobe level(SLL) quality of service(QOS)

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Rainfall Prediction using XGB Model with the Australian Dataset

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2024年 11卷 1-4页

作者： Vinta, Surendra Reddy Peeriga, Radhika VIT-AP University Andhra Pradesh Amaravati India Department of Computer Science & Engineering Marri Laxman Reddy Institute of Technology and Management Hyderabad Dundigal India

Rainfall prediction is a critical field of study with several practical uses, including agriculture, water management, and disaster preparedness. In this work, we examine the performance of several machine learning models in forecasting rainfall using a dataset of Australian rainfall observations from Kaggle. Six models are compared: random forest (RF), logistic regression (LogReg), Gaussian Naive Bayes (GNB), k-nearest neighbours (kNN), support vector classifier (SVC), and XGBoost (XGB). Missing value imputation and feature selection were used to preprocess the dataset. To analyse the models, we employed cross-validation and performance indicators such as accuracy, precision, recall, and F1-score. According to our findings, the RF and XGB models fared the best, with accuracy ratings of 87% and 85%, respectively. With accuracy ratings below 70%, the GNB and SVC models performed the poorest. Our findings imply that machine learning algorithms can be useful tools for predicting rainfall, but careful model selection and evaluation are required for reliable results. Copyright © 2024 S. R. Vinta et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

关键词： Nearest neighbor search

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Revolutionizing Education through Holistic Application of technology: A Blockchain-Powered Framework for Smart Learning

Revolutionizing Education through Holistic Application of Te...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Verma, Rohan Singh, Kiran Deep Singh, Prabh Deep Maurya, Sudhanshu Nerkar, Sanskruti Thapliyal, Nitin Department of Computer Science and Engineering Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Symbiosis Institute of Technology Nagpur Campus Pune India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350394412

The convergence of blockchain technology and education has ushered in a new era of innovation and transformation in the field of learning. This paper presents a comprehensive framework for integrating blockchain into the educational landscape, offering a blueprint for developing a more secure, transparent, and efficient smart education ecosystem. The framework encompasses key aspects such as identity management, content sharing, student records, micro-credentials, financial transactions, governance, data privacy, interoperability, user interfaces, and scalability. By exploring the potential benefits and challenges of blockchain adoption in education, this research paper seeks to inspire educators, institutions, and policymakers to embark on a journey toward a blockchain-enabled smart education future. As the world witnesses the growing importance of lifelong learning and digital credentials, this framework paves the way for a more accessible, equitable, and globally recognized education system poised to meet the evolving needs of learners in the 21st century. © 2024 IEEE.

关键词： Federated learning

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Land Use—Classification by Machine Learning Classifiers Using Landsat 8 Imagery

Land Use—Classification by Machine Learning Classifiers Usi...

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International Conference on Recent Advancements in Civil engineering, RACE 2022

作者： Thakur, Reena Panse, Prashant Department of Computer Science and Engineering Jhulelal Institute of Technology Nagpur India Department of Information Technology Medi-Caps University Indore India

ISBN: (纸本)9789819926756

Through the twentieth and first decades of the twenty-first century, quick and abandoned population growth, in combination with industrial and economic development, enhanced the frequency of land-use-land-cover change (LULCC) for several times, particularly in evolving nations. Moreover, the most effective ways to manage and analyze land transformation are the quantifiable valuation of changes in LULC, it is important to evaluate the comparison of the accuracy of different Land Use-Land Cover mapping algorithms in order to choose the good classifier for future earth observation and its applications. The QGIS tool is applied to consider minimal distance, maximum likelihood, and spectral angle mapping in this paper. Various classes have been explored, and training is carried out based on these to determine the accuracy of each method. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Landsat

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Face image authentication scheme based on MTCNN and SLT

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Multimedia Tools and Applications 2025年 1-43页

作者： Thabit, Rasha Al-Askari, Mohanad A. Mohammed, Dunya Zeki Anaam, Elham Abdulwahab Mahmood, Zainab H. Jabbar, Dina Jamal Salih, Zahraa Aqeel Information System Department College of Computer Sciences and Information Technology University of Al-Anbar Al-Anbar Iraq Computer Engineering Department College of Engineering Al-Iraqia University Baghdad Iraq Electronic and Communications Engineering Department College of Engineering Gilgamesh University Baghdad Iraq Faculty of Computing and Informatics Multimedia University Selangor Cyberjaya Malaysia Department of Cybersecurity Engineering College of Information Engineering Al-Nahrain University Baghdad Iraq Electrical Engineering Technical Collage Middle Technical University Baghdad Iraq Department of Business Administration Alamaal University College Baghdad Iraq

DeepFakes and face image manipulation methods have been widely distributed in the last few years and several techniques have been presented to check the authenticity of the face image and detect manipulation if exists. Most of the available manipulation detection techniques have been successfully applied to reveal one type of manipulation under specific conditions, however, many limitations and challenges can be encountered in this field. To overcome some limitations and challenges, this paper presents a new face image authentication (FIA) scheme based on Multi-Task Cascaded Conventional Neural Networks (MTCNN) and watermarking in Slantlet transform (SLT) domain. The proposed FIA scheme has three main algorithms that are face detection and selection, embedding, and extraction algorithms. Different block sizes have been used to divide the image into non-overlapping blocks followed by classifying them into two groups that are blocks from face area (FA) and blocks from the remaining area (RA) of the image. In the embedding algorithms, the authentication information is generated from FA blocks and embedded in the RA blocks. In the extraction algorithms, the embedded information is extracted from RA blocks and compared with the calculated data from FA blocks to reveal manipulations and localize the manipulated blocks if exist. Extensive experiments have been conducted to evaluate the performance of the proposed FIA scheme for different face images. The experimental work included tests for payload, capacity, visual quality, time complexity, and localization of manipulations. The results proved the efficiency of the proposed scheme in detecting and localizing different face image manipulations such as attributes attacks, retouching attacks, expression swap, face swap, and morphing attacks. The proposed scheme overcomes many limitations and it is 100% accurate in localizing the tampered blocks which makes it a better candidate for practical applications. © The Author(s), un

关键词： Authentication

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AI for Smart Agriculture - A Deep Learning Based Turmeric Leaf Disease Detection 9

AI for Smart Agriculture - A Deep Learning Based Turmeric Le...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Selvaraj, R. Devasena, M. S. Geetha Satheesh, T. Sathishkumar, C. Dr.N.G.P. Institute of Technology Department of Computer Science and Engineering Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Dr.N.G.P. Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Department of Computer Science and Engineering RVS Technical Campus-Coimbatore Coimbatore India

ISBN: (纸本)9798350377972

Plants is one of the major food sources for world population. In addition to that, some plants and their outcomes are used in food preparation. One of the plants which is widely used in foods and having huge medicinal value is turmeric. Curcuma long the botanically named spice turmeric has large anti-inflammatory properties, antioxidants, etc., the tropical perennial plant is cultivated worldwide. Diseases in plant leaves is one of the serious factors that affects the turmeric cultivation. Leaf disease reduces the production and it requires continuous monitoring to avoid total loss. Manual monitoring is challenging when the crop is planted in large fields. Due to industrial revolution and smart agriculture concepts, artificial intelligence- based disease detection procedure is presented in recent times. Thus, in this research work a deep learning-based turmeric leaf disease detection model is presented using AlexNet. The leaf disease classification model is experimented with our own dataset which includes diseases like Leaf Spot Disease, Leaf Blight, Leaf Rot, and Leaf Curl. The experimentation results provide the maximum accuracy of proposed leaf disease classification model as 95.5% which is better than traditional machine learning models. © 2024 IEEE.

关键词： AlexNet classification Deep learning Turmeric Leaf disease

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Adaptive 360° Video Streaming Over Wireless Communication Channels 9

Adaptive 360° Video Streaming Over Wireless Communication C...

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9th International Conference on Frontiers of Signal Processing, ICFSP 2024

作者： Valiandi, Ioanna Pattichis, Marios S. Kyriacou, Efthyvoulos Panayides, Andreas S. VIDEOMICS Group CYENS Centre of Excellence Nicosia Cyprus University of New Mexico Department of Electrical and Computer Engineering AlbuquerqueNM United States Cyprus University of Technology Department of Electrical Eng. Computer Engineering and Informatics Limassol Cyprus

ISBN: (纸本)9798350353235

360° video streaming is one of the prevalent communication technologies for enhancing user experience and has thus seen widespread adoption in virtual and mixed reality applications. However, delivering content at scale while securing the quality of wirelessly communicated 360° videos in real-time poses significant challenges. 360° videos come in ultra-high definition, necessitate unprecedented bitrate demands and involve high encoding complexity. The time-varying nature of underlying wireless channels further introduces a destabilizing factor, calling for video systems to seamlessly adjust to varying bandwidth throughput to maintain adequate quality of service and experience. To address this issue, in this study, we have developed a multi-objective optimization framework for real-time video encoding adaptation. The objective is to optimize both video quality and encoding efficiency while minimizing the required bitrate, subject to real-time application constraints. To achieve this, we relied on generating (offline) precise forward prediction models of video quality, bitrate demands, and encoding time, that can be used to select the optimum encoding configuration in real-time. To validate our methods, we implemented an adaptive video encoding controller, and ran emulations employing actual network traces from 5G mobile video streaming scenarios, using the popular open-source x264 and x265 codecs for video encoding. A dataset of 4K omnidirectional videos at 30 frames per second was used. © 2024 IEEE.

关键词： Video streaming

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Transfer Learning-based Emotion Detection System in Cultivating Workplace Harmony 20

Transfer Learning-based Emotion Detection System in Cultivat...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Shah, Premal P. Jadav, Nilesh Kumar Makwana, Vatsalkumar Trivedi, Harshal Bhatia, Jitendra Tanwar, Sudeep Shahinzadeh, Hossein Gujarat Technological University Adani Institute of Infrastructure Engineering Ahmedabad India Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad382481 India L. J. Institute of Engineering and Technology Department of Computer Engineering Ahmedabad India CEO Tushker AI Ahmedabad India Amirkabir University of Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350383942

The technological advancements made the organizations achieve more business profits and attain new horizons that significantly impact on employee stress and mental-well well-being. Moreover, due to the advent of Artificial intelligence (AI), which offers automotive tasks and efficiency gains, the employee get fear of losing their jobs. Due to this, the employee suffers from excessive stress and mental health issues. Therefore, emotion detection is imperative in such a stressful workplace environment. Inspired by this, we proposed a transfer learning-based emotion detection system to improve the workplace environment. For that, a facial emotion dataset is utilized, which comprises grayscale images of employee faces having emotions such as fear, neutral, anger, sadness, happiness, surprise, and disgust. Then, we used transfer learning-based pre-trained models with the intention of reducing the computational overhead of AI training. We employed ResNet, Inception, MobileNet, and EfficientNet which offer an effective accuracy while detecting the emotions of the employee. This strategic use of pre-existing models not only optimizes efficiency but also enhances the overall effectiveness of our facial emotion analysis system, ensuring a robust and accurate representation of diverse emotional states in employees. From the result analysis, we found that the ResNet outperforms other existing pre-trained models in terms of training accuracy (95.23%), training loss (0.41), and training time (2803 sec). © 2024 IEEE.

关键词： Learning systems

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