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Efficient allocation of independent gridlet on small, medium, and large grid

Personal and Ubiquitous computing 2023年第3期27卷 1029-1037页

作者： Rajeswari, D. Ramamoorthy, S. Srinivasan, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

Gridlet allocation in a computational grid environment is a major research issue to obtain not only the efficient gridlet allocation technique but also the time needed to obtain the efficient allocation technique. Grid computing networks have various nodes to process user jobs. To achieve the high performance of computational grid, task scheduling is an important issue. The users who are using services of grid systems are more cautious about time to complete their job. Hence, this work concentrates on gridlet allocation method used for time reduction by a genetic algorithm with the MapReduce programming model for independent tasks in computational grid. In computational grid environment, multi-objective problem formulation minimization of makespan and flowtime is considered. In this proposed technique, fitness function formulation for makespan and flowtime has been formulated mathematically. The genetic algorithm with the MapReduce programming model is implemented using MapReduce written in Java and then combined with GridSim. The experimental outcome with regard to time needed, flowtime, and makespan clearly reveals that the genetic algorithm with the MapReduce model effectively optimizes time, makespan, and flowtime in computational grid environment. A comparative study of performance efficiency among genetic algorithm with the MapReduce and sequential genetic algorithm (SGA) and parallel genetic algorithm (PGA) depicts the usefulness of the model. The execution time achieved by GA with the MapReduce model in small grid is 10.48 s, medium grid is 18.76 s, and large grid is 33.73 s. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： MapReduce

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Deep Neural Networks Based Feature Extraction with CBIR: An Exhaustive Analysis 1

Deep Neural Networks Based Feature Extraction with CBIR: An ...

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1st International Conference on Trends in Engineering Systems and Technologies, ICTEST 2024

作者： Praveena, S. Veeramakali, T. Srm Institute of Science and Technology Department of Data Science and Business Systems School of Computing Tamilnadu Kattankulathur India

ISBN: (数字)9798350307795

ISBN: (纸本)9798350307795

The need for having sufficient space to store and retrieve data has grown in importance in today's fast-paced worlde. Hence, end-users struggle to access particular facts, leading to difficulties in obtaining reliable statistics. The text-based image retrieval (TBIR) system is being enhanced by researchers to address these issues. Based on the user's query or annotation, TBIR finds relevant facts. This is the best method for users to get all the important data. The database unfortunately produces more unwanted data due to the complexity and length of the operation. A technology known as content-based image retrieval (CBIR), offers a way around this problem. The user's query or input images are used to determine the relevant output images. Machine learning and deep learning algorithms are highly suited for CBIR- based image classification and prediction. The notion of machine learning as a potential remedy for the semantic gap problem has been extensively studied. Given the recent successes of deep learning, there is evidence to suggest that we are capable of bridging the semantic gap in CBIR. The various techniques for feature extraction and similarity measurement are thoroughly reviewed in this paper. The benefits and drawbacks of deep learning networks and CBIR-based machine learning techniques are further investigated through comparison analysis portion. Consequently, detailed explanations of techniques utilizing deep learning for identifying skin disorders are provided. Following to that, a variety of CBIR approaches and the Corel 5K dataset are used to assess the various performance measures. © 2024 IEEE.

关键词： Feature extraction

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Comparison of the SOLA model and ARIMA for Cloud computing Resources Allocations 4

Comparison of the SOLA model and ARIMA for Cloud Computing R...

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4th International Multidisciplinary Information Technology and Engineering Conference, IMITEC 2024

作者： Prince Sekwatlakwatla, Sello Malele, Vusumuzi North-West University Unit for Data Science and Computing Department of Computer Science and Information Systems South Africa

ISBN: (纸本)9798350387988

Cloud computing solutions are becoming more and more popular as a way for organizations to improve productivity, save costs, and simplify procedures. The advantage of cloud services is that they enable users to store data remotely and access on-demand applications and services from a shared pool of configurable computing resources. Cloud computing resource allocation faces challenges in load balancing, effective resource management, and compliance with regulatory and legal requirements, requiring complex tools and techniques for optimal use. Most organizations are moving to cloud services and encouraging their clients to access services online. However, effective resource allocation is critical for improving performance and lowering costs in this area. Due to unpredictable network traffic in cloud computing, resource allocation is challenging, which causes customers to complain about application timeouts, delayed system times, and higher bandwidth use during peak hours. This entails allocating resources to various users and programs, such as memory, processing power, storage, and network bandwidth. In this regard, this study compares the ensemble method, which is stepwise Gaussian Linear Autoregressive (SGLA) and the individual method, which is autoregressive integrated moving average (ARIMA). The results show SGLA prediction accuracy increased with an average of 97.9%, and ARIMA prediction showed an accuracy of 71.5%. In this regard the ensemble method performed better than individual methods using the same datasets. The study recommends the ensemble method for the prediction and allocation of resources in cloud computing. © 2024 IEEE.

关键词： Cloud platforms

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Convolutional Neural Network-Based Multi-Fruit Classification and Quality Grading with a Gradio Interface

Convolutional Neural Network-Based Multi-Fruit Classificatio...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Nandhini, E. Vadivu, G. School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

Agricultural products are experiencing significant losses, especially after the harvesting phase, which is the period from fruit storage to the product reaching the consumer's hands. A lack of human resources, time consumption, product handling, and human errors during the post-harvest stage of the fruit grading and sorting process result in huge losses. Deep learning (DL) offers an automated process for classifying and identifying fruits. Developing an architecture for a VGG16 convolutional neural network (CNN) within the Gradio user interface, utilizing five distinct fruit types as the model's dataset, such as apple, banana, guava, orange, apple, and pomegranate. For five different sets of fresh and defect fruits, the proposed model got 91.1% and 89.1% of test accuracy for multi-fruit clas- sification and fruit status prediction, respectively. This machine- based automated model has the potential to reduce losses and enhance fruit grading and sorting efficiency, thereby significantly impacting sustainable and productive agricultural industries through increased productivity. © 2024 IEEE.

关键词： Convolutional neural networks

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Gesture-driven Innovation: Exploring the Intersection of Human-Computer Interaction and Virtual Fashion Try-On Systems 2

Gesture-driven Innovation: Exploring the Intersection of Hum...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Prakash, M. Arunkumar, Nithes Gokul, G. SRM Instititue of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulathur603203 India

ISBN: (纸本)9798350365269

Virtual Fashion Try-On systems revolutionize online fashion retail, offering immersive shopping experiences. This paper investigates integrating gesture-driven interaction into these systems to enhance engagement, realism, and usability. The paper highlights the importance of Virtual Fashion Try-On systems in addressing fit uncertainty and tactile interaction limitations in online shopping. It explores gesture-driven interaction to enrich virtual fashion experiences. The paper examines gesture recognition, covering hand gestures, body movements, and facial expressions. Various algorithms and techniques, including computer vision and machine learning, are discussed, addressing challenges like accuracy and real-time performance. Challenges and future directions in gesture-driven Virtual Fashion Try-On are discussed, covering technical hurdles, human-centered design, and ethical considerations. Recommendations for future research and development are outlined. © 2024 IEEE.

关键词： Virtual reality

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P3DC:Reducing DRAM Cache Hit Latency by Hybrid Mappings

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Journal of Computer science & Technology 2024年第6期39卷 1341-1360页

作者： Ye Chi Ren-Tong Guo Xiao-Fei Liao Hai-Kun Liu Jianhui Yue National Engineering Research Center for Big Data Technology and System Wuhan 430074China Services Computing Technology and System Laboratory Wuhan 430074China Cluster and Grid Computing Laboratory Wuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China School of Big Data and Internet Shenzhen Technology UniversityShenzhen 518118China Department of Computer Science Michigan Technological UniversityHoughton 49931-1295U.S.A.

Die-stacked dynamic random access memory(DRAM)caches are increasingly advocated to bridge the performance gap between the on-chip cache and the main *** fully realize their potential,it is essential to improve DRAM cache hit rate and lower its cache hit *** order to take advantage of the high hit-rate of set-association and the low hit latency of direct-mapping at the same time,we propose a partial direct-mapped die-stacked DRAM cache called *** design is motivated by a key observation,i.e.,applying a unified mapping policy to different types of blocks cannot achieve a high cache hit rate and low hit latency *** address this problem,P3DC classifies data blocks into leading blocks and following blocks,and places them at static positions and dynamic positions,respectively,in a unified set-associative *** also propose a replacement policy to balance the miss penalty and the temporal locality of different *** addition,P3DC provides a policy to mitigate cache thrashing due to block type *** results demonstrate that P3DC can reduce the cache hit latency by 20.5%while achieving a similar cache hit rate compared with typical set-associative caches.P3DC improves the instructions per cycle(IPC)by up to 66%(12%on average)compared with the state-of-the-art direct-mapped cache—BEAR,and by up to 19%(6%on average)compared with the tag-data decoupled set-associative cache—DEC-A8.

关键词： die-stacked dynamic random access memory(DRAM) cache set-associative direct-mapped hit latency

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Hybrid intelligent system for channel allocation and packet transmission in CR-IoT networks

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International Journal of Hybrid Intelligent Systems 2024年第2期20卷 101-117页

作者： Asuquo, Daniel E. Umoh, Uduak A. Robinson, Samuel A. Dan, Emmanuel A. Udoh, Samuel S. Attai, Kingsley F. Department of Information Systems Faculty of Computing University of Uyo TETFund Center of Excellence in Computational Intelligence Research University of Uyo Department of Cyber Security Faculty of Computing University of Uyo Department of Computer Science Faculty of Computing University of Uyo Department of Data Science Faculty of Computing University of Uyo Department of Mathematics and Computer Science Ritman University Ikot Ekpene

The proliferation of interconnected devices is driving a surge in the demand for wireless spectrum. Meeting the need for wireless channel access for every device, while also ensuring consistent quality of service (QoS), poses significant challenges. This is particularly true for resource-limited heterogeneous devices within Internet of Things (IoT) networks. Cognitive radio (CR) technology addresses the shortcomings of traditional fixed channel allocation policies by enabling unlicensed users to opportunistically access unused spectrum belonging to licensed users. This facilitates timely and reliable transmission of mission-critical data packets. A cognitive radio-enabled IoT (CR-IoT) network is poised to better accommodate the growing demands of diverse applications and services within the smart city framework, spanning areas such as healthcare, agriculture, manufacturing, logistics, transportation, environment, public safety, and pharmaceuticals. To minimize switching delays and ensure energy and spectral efficiency, this study proposes a hybrid intelligent system for efficient channel allocation and packet transmission in CR-IoT networks. Leveraging Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS), the system dynamically manages spectrum resources to minimize handoffs while upholding QoS. A Java-based simulation integrates system outputs with interference temperature data to accommodate service demands across 2G-4G spectrums. Evaluation reveals SVM's 98.8% accuracy in detecting spectrum holes and ANFIS's 90.4% accuracy in channel allocation. These results demonstrate significant potential for enhancing spectrum utilization in various IoT applications. © 2024-IOS Press. All rights reserved.

关键词： Cognitive radio network intelligent whitespace detection internet of things optimized channel allocation packet transmission

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An Intelligent Machine Learning Approach to Detect Spam in Social Media 5

An Intelligent Machine Learning Approach to Detect Spam in S...

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5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Mohandas, R. Prasanna, D.S. John Deva Meenakshi, N. Kaviyaraj, R. Nathiya, S. Arivazhagan, N. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur India Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulathur India

ISBN: (纸本)9798350389609

Machine learning based intelligent approach is applied to finding spam in YouTube videos. Spam comments are ones that are promotional or unrelated. A growing number of users have been drawn to the idea of making money by placing advertisements on popular social networking platforms over time. These remarks damage a channel's reputation. YouTube has addressed the issue with a limited set of solutions that concentrate on preventing comments from including links. Such heuristics are now futile since spammers have found ways to get around them. Despite the fact that common machine learning classification techniques have demonstrated considerable success, new methods can still increase accuracy. Therefore, this research attempt to identify such statements using machine learning techniques like Random Forest, SVM, and Naive Bayes. In order to convey a clear concept, graphs are used to illustrate data cleaning, data analysis, text preparation, and model building in this research. © 2024 IEEE.

关键词： Adversarial machine learning

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SECOND-ORDER OPTIMALITY CONDITIONS FOR NONSMOOTH MULTIOBJECTIVE OPTIMIZATION SUBJECTED TO MIXED CONSTRAINTS

Applied Set-Valued Analysis and Optimization

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Applied Set-Valued Analysis and Optimization 2025年第1期7卷 39-55页

作者： Tung, Nguyen Minh Cuong, Nguyen Minh Department of Data Science of Business Ho Chi Minh University of Banking Ho Chi Minh City Viet Nam Department of Mathematics and Computing University of Science Vietnam National University Ho Chi Minh City Viet Nam

This paper provides some second-order optimality conditions for local weak and strict efficient solutions to a nonsmooth multiobjective optimization problem subjected to both equality and inequality constraints. We first propose a type of second-order Abadie constraint qualification (CQ) and give a sufficient condition for this CQ with the help of the property of local error bound. Then, we employ it to obtain primal necessary conditions. A primal sufficient condition is also established. We next employ some alternative theorems to obtain the corresponding dual results. Applications to nonsmooth fractional programming problems are given. Finally, some examples are provided to illustrate our results. ©2025 Applied Set-Valued Analysis and Optimization.

关键词： Optimization algorithms

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Dental Biometrics Segmentation on Panoramic X-Ray Images using Computational Intelligence Approach 14

Dental Biometrics Segmentation on Panoramic X-Ray Images usi...

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14th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Sujithra, M. Rathika, J. Velvadivu, P. Abinanda, P. Gayathri, G. Rekha, V.S. Department of Computing – Data Science Coimbatore Institute of Technology Coimbatore India

Dental Biometric is a new field of study in the sector of biometrics identification. This technique can be sometimes used instead of the usual fingerprint biometric identification. In most of the cases, Dental Biometric credentials come handy to analyze the details of a dead person where, the Dental Biometrics of the person before death and the person after their death could potentially explain various reasons for their death and could justify their identity after death. Neural Networks have the potential to learn about anything using their complex construction structure. Here, x-ray copies of the dental teeth structure of a potential individual are given or fed to the network and the neural network with the help of an object detection platform such as OpenCV2 detects the teeth structure and can visualize the teeth structure with x-ray of that individual alone. As the neural is now able to see the teeth, it can learn a lot of crucial details about it from the picture it can understand. The interpretation about the teeth contours and the number of teeth is made along the way. The proposed methodology has better accuracy than the fuzzy clustering relevant methods. Also Suggested to use the appropriate values of parameters that should be opted for the algorithm. © Grenze Scientific Society, 2023.

关键词： Object detection

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