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A Novel Predictive Model for Edge Computing Resource Scheduling Based on Deep Neural Network

computer Modeling in engineering & sciences 2024年第4期139卷 259-277页

作者： Ming Gao Weiwei Cai Yizhang Jiang Wenjun Hu Jian Yao Pengjiang Qian School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi214122China The Center for Intelligent Systems and Applications Research School of Artificial Intelligence and Computer ScienceJiangnan UniversityWuxi214122China School of Information Engineering Huzhou UniversityHuzhou313000China

Currently,applications accessing remote computing resources through cloud data centers is the main mode of operation,but this mode of operation greatly increases communication latency and reduces overall quality of service(QoS)and quality of experience(QoE).Edge computing technology extends cloud service functionality to the edge of the mobile network,closer to the task execution end,and can effectivelymitigate the communication latency ***,the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management,and the booming development of artificial neural networks provides us withmore powerfulmethods to alleviate this ***,in this paper,we proposed a time series forecasting model incorporating Conv1D,LSTM and GRU for edge computing device resource scheduling,trained and tested the forecasting model using a small self-built dataset,and achieved competitive experimental results.

关键词： Edge computing resource scheduling predictive models

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Brain Tumor Detection: A Review on Pre-deep Learning and Deep Learning Era 15th

Brain Tumor Detection: A Review on Pre-deep Learning and Dee...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Agilandeeswari, L. Akshat Dubey, Dhruv Mutyalabhuvan, Saieesh School of Computer Science Engineering and Information Systems VIT Vellore Vellore India

ISBN: (纸本)9783031810794

For medical analysis and interpretation, MR brain image categorization that is automated and accurate is essential. The most significant part of the human body that MRI, or magnetic resonance imaging, allows us to see in great detail is the brain. It provides very important information that helps in the clinical diagnosis of tumors and helps in biomedical research field. Tumors can be detected through ultrasound techniques, CT scans and MRIs. However, using the MRI images would prove to be fruitful because the accuracy rate will increase. AI can help in providing precise information about brain tumor to doctors. In this paper, we presented a brain tumor detection system on pre-deep learning and during deep learning era. From the study, it is clear evident that compared to pre-deep learning, deep learning era produces a better results. The performance of the suggested models are evaluated in terms of sensitivity, accuracy, efficiency, and specificity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neuroimaging

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Leveraging Transfer Learning for Robust Detection of Malicious Network Activity 15th

Leveraging Transfer Learning for Robust Detection of Malicio...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Agilandeeswari, L. Chunduri, Atirath Kunchakuri, Rithesh School of Computer Science Engineering and Information Systems VIT Vellore Vellore India

ISBN: (纸本)9783031810824

This paper explores a novel method in computer security by improving network threat detection through transfer learning. We created a model that effectively recognizes possible dangers by analyzing network behavior using transfer learning. Our model is a reliable tool for detecting network threats because of its remarkable high accuracy and low error rates. Our tests demonstrated the model's great efficacy, consistently improving its ability to recognize threats with few errors. This is an important improvement for network security since it shows that transfer learning is an effective technique, particularly in situations where there is a lack of available data for learning. Our work is important because it can lead to better online security. Our demonstration of the effective application of transfer learning in this domain opens up new avenues for cybersecurity research and development in the future. In today's digital environment, where threats are continually evolving, research like this is extremely crucial for combating them. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Network intrusion

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Convolutional Neural Network (CNN) Classifiers Used in Land Use/Land Cover Monitoring and Classification: A Review 23th

Convolutional Neural Network (CNN) Classifiers Used in Land ...

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23rd International Conference on Intelligent systems Design and Applications, ISDA 2023

作者： Prabukumar, M. Agilandeeswari, L. Prabhavalkar, Sushant Prashant School of Computer Science Engineering and Information Systems VIT Vellore India

ISBN: (纸本)9783031647758

Land Use/Land Cover Analysis is conducted to perform continuous monitoring and assessment of land resources in order to manage the Land Usage and Land Utilization and to take the right decisions to protect the environment. This article discusses various techniques/algorithms used to build robust models to perform Land Use/Land Cover Classification. The article includes various architecture models including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Residual Networks (RN), etc. It also includes various Accuracy Measurement Metrics, proposed models, and corresponding issues. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Recurrent neural networks

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Automatic Text Summarization for Medical Dataset-An Analysis 23th

Automatic Text Summarization for Medical Dataset-An Analysis

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23rd International Conference on Intelligent systems Design and Applications, ISDA 2023

作者： Agilandeeswari, L. Dagar, Akash Deepthi, A. Arangasakthivel, R. School of Computer Science Engineering and Information Systems VIT Vellore India

ISBN: (纸本)9783031648120

This review paper explores the application of deep learning techniques in abstractive text summarization, with a focus on their relevance to medical datasets. Abstractive text summarization is a crucial area of natural language processing, with the potential to significantly impact the field of healthcare and medical research. The paper provides a concise and crisp examination of the various deep learning models, methods, and algorithms employed in generating abstractive summaries from medical texts. It also highlights the challenges, trends, and potential future directions in this domain. The primary objective of this review is to offer insights into the current state-of-the-art methods for abstractive text summarization in the medical domain and their implications for healthcare professionals, researchers, and data scientists. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Information management

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Transfer Learning based Framework of VGG16 to Detect Breast Cancer using Histopathological Images 6

Transfer Learning based Framework of VGG16 to Detect Breast ...

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6th Doctoral Symposium on Intelligence Enabled Research, DoSIER 2024

作者： Divya, Zion G. Tripathy, B.K. School of Computer Science and Engineering VIT TN Vellore632014 India School of Computer Science Engineering and Information Systems VIT TN Vellore632014 India

Breast Cancer Detection introduces a prominent confrontation for researchers and clinical experts as it is one of the major public health issues and is weighed as a leading root for cancer correlated deaths among women worldwide. Early diagnosis helps the chances of survival rate which is a crucial part, but standard breast cancer classical techniques rely on operative techniques, open procedures and time-consuming analyses. So, the standard classical techniques pave a way in demanding accurate solutions that are provided with efficient algorithms. This paper addresses these challenges by utilizing automated cancer detection that presents an ensemble deep learning approach by implementing transfer learning for breast cancer detection using histopathological images for classifying Benign and Malignant tissues. Transfer learning uses VGG16 to train the model on large dataset, by reusing the knowledge that has obtained from previous task which is considered as an input to another task thereby improving the performance. In this paper VGG16 architecture is considered as a pretrained model to train on ImageNet. Ensemble strategy is applied as a next step by taking the average of predicted probabilities, out of which the VGG16 model offers overall best accuracy of 98.83%. © 2025 Copyright for this paper by its authors.

关键词： CNN Deep Learning Transfer Learning VGG16

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Effect of Graphite Addition on Microstructure, Mechanical Properties and Thermal Properties of Injection Molded AZ91D Alloy+1

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Materials Transactions 2024年第4期65卷 374-380页

作者： Hideshima, Yasutoshi Maeda, Fumiya Fukuta, Tadao Ozaki, Koichi Course of Advanced Systems Engineering Graduate School of Computer Science and Systems Engineering Okayama Prefectural University Soja719-1197 Japan Process Technology Development Department Technology Development Division Seiko Epson Corp. Suwa399-0211 Japan Department of Systems Engineering Faculty of Computer Science and Systems Engineering Okayama Prefectural University Soja719-1197 Japan

Magnesium chips were coated with a high concentration of graphite using a binder and were used as the raw material for injection molding. The microstructure of the magnesium injection-molded product with added graphite exhibited a dispersion of needle-like graphite particles. No significant voids were observed at the interfaces between the graphite and the matrix. The addition of more than 0.5 mass% graphite decreased the proof stress and tensile strength of the injection-molded products. The Young’s modulus of the graphite-added products tended to decrease with an increase in the graphite content, which is consistent with the lower limit of the rule of mixtures. The thermal conductivity of the 6.9 mass% graphite-added product increased compared with that of the AZ91D magnesium alloy and the coefficient of linear thermal expansion decreased. Both values are within a range that satisfied the rules of mixtures. © 2024 The Japan Institute of Metals and Materials.

关键词： Thermal expansion

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Secure location-aware geocast routing in Internet of Vehicles

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Multimedia Tools and Applications 2025年第18期84卷 19669-19692页

作者： Kumar, Sunil Dohare, Upasana Kumar, Sushil Department of Computer Science and Information Technology Central University of Haryana Mahendragarh India School of Computing Science & Engineering Galgotias University Greater Noida India School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

Efficient message dissemination in Vehicular Ad Hoc Networks (VANETs) relies on robust connectivity between neighboring vehicular nodes, yet it is often compromised by malicious intruders. While recent literature proposes various approaches to enhance geocasting performance, security considerations for connectivity are often overlooked. To address this gap, we propose the Secure Location-Aware Cache Agent based Geocast Routing (SL-CAGR) protocol, emphasizing an Intrusion Prevention Technique (IPT) for message integrity preservation and a Connectivity Reliant Packet Forwarding (CRPF) algorithm for improved VANET connectivity. Our protocol introduces a network model for cache agent-based geocast routing on the Internet of Vehicle (IoV) environment, implements IPT using Diffie-Hellman digital signatures, and presents a secure geocast routing strategy focusing on re-caching and cache agent selection. Empirical simulations demonstrate SL-CAGR's superiority in packet delivery ratio, packet loss, duplication, hop count, end-to-end delay, and network load compared to these protocols. Simulation results demonstrate that SL-CAGR outperforms state-of-the-art techniques like CAG, CTRC, and D-Flooding. SL-CAGR stands out by merging intrusion prevention, secure geocast routing, and cache agent selection, ensuring robust security and efficiency in VANETs. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Vehicular ad hoc networks

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DFNet: Dense fusion convolution neural network for plant leaf disease classification

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Agronomy Journal 2024年第3期116卷 826-838页

作者： Faisal, Muhamad Leu, Jenq-Shiou Avian, Cries Prakosa, Setya Widyawan Köppen, Mario Department of Electronic and Computer Engineering (ECE) National Taiwan University of Science and Technology Taipei City Taiwan Department of Computer Science and Systems Engineering (CSSE) Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Iizuka-shi Japan

The early identification of plant diseases is crucial for preventing the loss of crop production. Recently, the advancement of deep learning has significantly improved the identification of plant leaf diseases. However, most approaches depend on a single convolutional neural network (CNN) to extract the leaf features, ignoring the opportunity to take full advantage of the feature richness available in the images. This paper explores a novel CNN model with multiple automated feature extractors, namely, dense fusion CNN (DFNet), for classifying plant leaf diseases. DFNet aims to increase the diversity of extracted features in order to improve discrimination. Instead of using a single-CNN model, DFNet relies on a double-pretrained CNN model, MobileNetV2 and NASNetMobile, as the feature extractor. The features extracted from each CNN are fused in the fusion layer using a fully connected network. The proposed method was evaluated using corn (Zea mays L.) and coffee (Coffea canephora) leaf disease datasets and compared to the existing models. The experiment showed that DFNet is superior and consistent to other CNN methods by achieving an accuracy of 97.53% for corn leaf diseases and 94.65% for coffee leaf diseases. © 2023 The Authors. Agronomy Journal © 2023 American Society of Agronomy.

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Automatic emotion recognition using deep neural network

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Multimedia Tools and Applications 2025年 1-30页

作者： Sujatha, R. Chatterjee, Jyotir Moy Pathy, Baibhav Hu, Yu-Chen School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of CSE Graphic Era University Dehradun India School of Electrical Engineering Vellore Institute of Technology Vellore India Department of Computer Science Tunghai University Taichung City Taiwan

Emotions are a vital semantic part of human correspondence. Emotions are significant for human correspondence as well as basic for human–computer cooperation. Viable correspondence between people is possibly achieved when both the importance and the emotion of the correspondence are perceived by all groups included. Understanding the significance of language has generally been concentrated on in natural language processing (NLP) as a semantic examination. In NLP, the text can be handled appropriately for classification. Emotion detection from facial emotion is the subfield of social signal processing applied in a wide assortment of regions, explicitly for human and PC collaboration. Many researchers have proposed various approaches, generally utilizing machine learning concepts. Automatic emotion recognition (AER) is significant for working with consistent intuitiveness between a person and a smart device toward fully acknowledging an intelligent society. Many researchers examined cross-lingual and multilingual speech emotion as a stage toward language-free emotion acknowledgment in natural speech. In the present work, we are proposing a deep learning-based AER system using four openly accessible datasets, namely Basic Arabic Vocal Emotions Dataset (BAVED), Acted Emotional Speech Dynamic Database (AESDD), Urdu written in Latin/Roman Script (URDU), and Toronto Emotional Speech Set (TESS), by utilizing the Jupyter notebook and a Python library for music and audio synthesis named Librosa. The experimental results exhibited that the proposed approach achieves better than the existing approaches, i.e., the accuracy of the proposed system with the URDU dataset is 96.24%, the TESS dataset is 99.10%, the AESDD dataset is 65.97%, and the BAVED dataset is 73.12%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantics

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