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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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A Comprehensive Analysis of Image Processing Methods for Agricultural Product Quality Control

A Comprehensive Analysis of Image Processing Methods for Agr...

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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

In the post-harvest stages of agricultural products, labor shortages and poor-quality control lead to significant market losses. The automated industries for agricultural products that use machine learning are evolving, with a recent focus on deep learning (DL). Computer vision (CV) serves as an essential part in the automation method for fruits and vegetables, where image processing (IP) ensures proper product categorization, addressing the issue of product loss. This paper focuses on IP techniques in sequential procedures, which can improve efficiency and precision in agricultural product sorting and grading. Deep neural networks and algorithms are effective because they achieve high classification accuracy with a low loss percentage. © 2024 IEEE.

关键词： Industries Accuracy Reviews Image processing Scalability Quality control Loss measurement IP networks Reliability Sorting

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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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Predicting Employee Churn- data Driven Approach for Retention Management

Predicting Employee Churn- Data Driven Approach for Retentio...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Jayaraj, R. Mehrotra, Safal Kumar, Harshit School of Computing Srm Institute of Science and Technology Dept. of Data Science and Business Systems Chennai India

ISBN: (纸本)9798331510022

Employee churn is a critical challenge faced by organizations across industries, leading to disruptions in productivity and increased costs associated with recruitment and training. In this study, we propose an effective solution leveraging Random Forests to predict employee churn accurately and efficiently. Our approach addresses the inherent complexities of employee turnover prediction while considering resource constraints commonly encountered in practical applications. The proposed model incorporates feature engineering techniques and ensemble learning principles to harness the predictive power of Random Forests. Through a comprehensive evaluation on real-world employee datasets, we demonstrate the superior performance of our model in comparison to existing state-of-the-art methods. Our analysis encompasses various evaluation metrics, including accuracy, precision, recall, and F1-score, showcasing the robustness and reliability of our model across diverse organizational contexts. © 2024 IEEE.

关键词： Contrastive Learning

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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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Effortless learning: leveraging summarization and tabulation for automated note making

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Multimedia Tools and Applications 2024年 1-26页

作者： Arivarasi, A. Srinivasan, R. Rajeswari, D. Govindasamy, Alagiri School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India Future Connect Technologies Pvt. Ltd Tamil Nadu Kanchipuram600034 India

In this fast-paced Digital Age, Natural Language Processing (NLP) can prove beneficial in consuming quality information efficiently. With the ever-growing number of learning resources, it is becoming onerous for students to choose the best-suited learning resource. Furthermore, many students find it difficult to understand and retain highly factual information. To overcome these issues, this paper proposes a novel Automated TExt Summarization of scholastic text in Table format (Auto-TEST) to provide information at a glance by mimicking the student’s style of note-making. The Auto-TEST framework is achieved through two steps which is Summarization and Tabulation. Initially, the Extractive Single Document Text Summarization (ESDTS) method is adopted to identify the salient information from the non-fictional (scholastic) text which is extracted and grouped together to form a concise summary. A Named Entity Recognition (NER) method is implemented to extract noun chunks and then arrange them in a tabulation format that aids in memorizing essential facts and figures by enabling effortless retrieval. The performance of the Auto-TEST framework is gauged by using scholastic domain-specific dataset using ROUGE as an evaluation metric. The extracted entities gives an average Recall of 71.40%, an average Precision score of 75.80% and an average F-score of 73.54% for the dataset respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Students

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A Hybrid Relational Approach Toward Stock Price Prediction and Profitability

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5844-5854页

作者： Patel, Manali Jariwala, Krupa Chattopadhyay, Chiranjoy Sardar Vallabhbhai National Institute of Technology Department of Computer Science and Engineering Surat395007 India FLAME University School of Computing and Data Sciences Pune412115 India

An accurate estimation of future stock prices can help investors maximize their profits. The current advancements in the area of artificial intelligence (AI) have proven prevalent in the financial sector. Besides, stock market prediction is difficult owing to the considerable volatility and unpredictability induced by numerous factors. Recent approaches have considered fundamental, technical, or macroeconomic variables to find hidden complex patterns in financial data. At the macro level, there exists a spillover effect between stock pairs that can explain the variance present in the data and boost the prediction performance. To address this interconnectedness defined by intrasector stocks, we propose a hybrid relational approach to predict the future price of stocks in the American, Indian, and Korean economies. We collected market data of large-, mid-, and small-capitalization peer companies in the same industry as the target firm, considering them as relational features. To ensure efficient feature selection, we have utilized a data-driven approach, i.e., random forest feature permutation (RF2P), to remove noise and instability. A hybrid prediction module consisting of temporal convolution and linear model (TCLM) is proposed that considers irregularities and linear trend components of the financial data. We found that RF2P-TCLM gave the superior performance. To demonstrate the real-world applicability of our approach in terms of profitability, we created a trading method based on the predicted results. This technique generates a higher profit than the existing approaches. © 2024 IEEE.

关键词： Profitability

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Deep Learning-Based Emotion Recognition using CNN, LSTM for Multimodal Text, Speech, and Facial Analysis 5

Deep Learning-Based Emotion Recognition using CNN, LSTM for ...

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

作者： Firdou, Talat Binti Ashra, Mohammed Kalpan, A.V. School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350389609

Emotion detection is a critical field in AI as well as computer vision, seeking to recognize or detect human emotions from facial expressions. Although this area has been greatly advanced through Convolutional Neural Networks (CNNs), current methods using only single-modal data often exhibit reduced accuracy. In this work, we confront these challenges with a hybrid deep learning method for disparate modalities of information such as speech, video, and text. We pre-process and then use CNN feature extractors for the FER2013 data set. The hybrid deep learning model, hence proposed, achieved a weighted accuracy of 79.3% on FER2013, overcoming the conventional unimodal methods. We are using transformers library with the pre-trained RoBERTa model for text. For speech emotion features, we are using TESS dataset then using a CNN and LSTM (Long Short-Term Memory) model. The CNN extracts feature from the speech, while the LSTM is used for temporal analysis to detect emotions. Once these multimodal features have been obtained, our work would be to fuse in order to get the final emotion recognition using Deep Neural Networks (DNNs). The process increases the precision and reliability of the recognition systems, helping to accumulate even more detailed intuition for different emotional states for a combined approach. © 2024 IEEE.

关键词： Convolutional neural networks

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PulseSync BP: A Neural Network Framework for Non-Contact Blood Pressure Estimation

PulseSync BP: A Neural Network Framework for Non-Contact Blo...

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2024 International Conference on Advances in data Engineering and Intelligent computing Systems, ADICS 2024

作者： Ghosh, Tanumay Singal, Omisha Lodh, Namit Suchitra, S. School Of Computing Srm Institute Of Science And Technology Department Of Data Science And Business Systems Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350364828

Using a cutting-edge neural network framework, 'PulseSync BP' estimates blood pressure without contact. Using photoplethysmogram (PPG) signals and other physiological data, this novel model uses advanced signal processing. The suggested framework changes blood pressure monitoring by eliminating cuff-based measures. PulseSync BP's RNN architecture is carefully designed for optimal prediction accuracy. The algorithm effectively estimates blood pressure through extensive experimentation, demonstrating its potential for continuous healthcare applications. This framework's capacity to use various physiological cues shows its adaptability and versatility. PulseSync BP allows real-time, non-intrusive blood pressure monitoring, advancing individualized health tracking solutions. Cuffless blood pressure estimate is advanced here, demonstrating how artificial intelligence could improve healthcare accessibility and patient-centric monitoring. PulseSync BP and other revolutionary technologies can transform blood pressure monitoring in healthcare. © 2024 IEEE.

关键词： Blood pressure

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E-Evaluation Based Authentication for Virtual Discussion on an Online Platform 2

E-Evaluation Based Authentication for Virtual Discussion on ...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rajkumar, K. Arikumar, K.S. Ragupathi, T. School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350371055

The main aim of this work is to develop an Online Discussion platform for users. The platforms will become more mature while having more users. In the paper, a sophisticated strategy is suggested to tackle a critical challenge by presenting an effective and secure method for authentication using e-evaluation. The proposal doesn't require extra elements and considers continuous authentication through the random generation of a variable number of questions derived from the gathered information. This application works in various types of browsers as well as in various networks. Many users can use this Discussion platform at the same time. The IP address plays the main role in this application. Discussion platforms and conversational interfaces as they are also known provide a new way for users to interact with other users. A Discussion platform user to share their thoughts and feelings in the same manner that they might address a person. Online discussion forums are considered associate degree extension of ancient learning that promotes dialogue, reflection, information construction, and self-assessment. Given their Potential advantages, online discussion forums are widely adopted as a tool for online learning However, Discussion platforms are currently being adopted at a high rate on computer chat platforms. © 2024 IEEE.

关键词： chats idea knowledge post thoughts Virtual Discussion platform

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