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SHERA: SHAP-Enhanced Resource Allocation for VM Scheduling and Efficient Cloud Computing

IEEE Access 2025年 13卷 92816-92832页

作者： Slathia, Ashwin Singh Sharma, Abhiram Krishna, P.B. Anand, Saksham Rathi, Ayush Joseph, Linda Gao, Xiao-Zhi Vellore Institute of Technology School of Computer Science and Engineering Chennai600127 India University of Eastern Finland School of Computing Kuopio Finland

Cloud computing plays a crucial role in modern technology, providing scalable and on-demand computing resources. However, excessive resource use can result in higher energy demand, higher operating expenses, and a more significant adverse effect on the environment as per study by A. Berl et al. [1]. In order to forecast and maximize the energy efficiency in cloud systems, this paper presents a machine learning based methodology. Using the Cloud Efficiency Dataset from Google Cloud, key performance indicators such as CPU usage, memory consumption, network traffic, and power consumption were analyzed. Three machine learning models—Random Forest, Naïve Bayes, and Support Vector Machine (SVM) were trained and assessed based on Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and an Equivalent Accuracy metric. Among them Random Forest model performed the best, achieving an RMSE of 0.16 and an accuracy of 96.8 %. To improve the interpretability of the model Explainable Artificial Intelligence (XAI) techniques were applied, specifically SHapley Additive exPlanations (SHAP), to evaluate feature importance. The findings demonstrated that the most important variables which influence energy efficiency are CPU usage and memory consumption. This research develops A novel algorithm based on the XAI results. Unlike static thresholding or heuristic schedulers, our SHAP-driven approach dynamically adapts VMplacement decisions with built-in interpretability. It is capable to efficiently schedule and allocateVirtual Machines on the cloud in a live environment. The proposed framework demonstrates tthe high scalability and real time applicability, making it suitable for the deployment in live cloud environments. While the integration of XAI methods like SHAP enhances interpretability, it introduces additional computational overhead—a trade-off that is justified by the improved transparency and decision making support. © 2013 IEEE.

关键词： Resource allocation

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Detecting AI-Generated Deep Fakes Using ResNext CNN and LSTM-Based RNN: A Robust Approach for Real-Time Video Manipulation Detection

Detecting AI-Generated Deep Fakes Using ResNext CNN and LST...

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International Conference on. Cryptology and Network Security with Machine Learning, ICCNSML 2023

作者： Dhar, Akanksha Agrawal, Ekansh School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9789819706402

The increasing computational power has greatly empowered deep learning algorithms, making the creation of highly realistic and virtually undetectable synthetic videos, commonly known as deep fakes, remarkably simple. These deep fakes, often featuring convincing face swaps, can be employed in situations such as generating political turmoil, fabricating terrorism events, distributing revenge porn, and engaging in blackmail. In this study, we introduce an innovative deep learning-based technique designed to effectively differentiate between AI-generated fake videos and genuine ones. Our approach excels at automatically identifying manipulated videos, including those involving content replacement and reenactment deep fakes. Our endeavor involves leveraging Artificial Intelligence (AI) to combat its own creations. The central component of our system utilizes a ResNext Convolutional Neural Network (CNN) to extract features from individual frames of videos. These features are then used to train a Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN). This RNN helps classify videos by determining if they have been manipulated, such as being deepfakes, or if they are genuine recordings. To ensure our model performs well with real-time data and reflects real-world situations, we evaluate it on a large and balanced dataset created by combining various existing datasets like FaceForensic++, Deepfake Detection Challenge, and Celeb-DF. Furthermore, we showcase that our system achieves competitive results using a straightforward yet effective approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

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Flood Survivor Detection through Image Fusion and Yolo Model 3

Flood Survivor Detection through Image Fusion and Yolo Model

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Vinothini, R. School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu 632014 India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361186

Natural disasters like flood happen when water overtakes normally dry places. During rescue missions aimed at saving lives, rescue teams responding to floods encounter numerous obstacles. Locating and discovering survivors is a vital component of rescue operations. For quick flood survivor recognition, the suggested approach combines image fusion with the YOLO (You Only Look Once) model, offering a novel method. This model utilizes image fusion by merging data from various sources, including RGB and Near Infrared Red (NIR) images captured by Unmanned Aerial Vehicles (UAVs). In order to efficiently identify flood survivors and aid rescue crews in their effort to preserve lives, the resulting fused image is processed using the YOLO model. © 2024 IEEE.

关键词： Image fusion

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Optimal feature extraction and classification-oriented medical insurance prediction model: machine learning integrated with the internet of things

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International Journal of computers and Applications 2022年第3期44卷 278-290页

作者： Chowdhury, Subrata Mayilvahanan, P. Govindaraj, Ramya School of Computer Science Vels Institute of Science Technology & Advanced Studies TN Chennai India School of Information Technology and Engineering Vellore Institute of Technology TN Vellore India

This paper plans to develop an effective machine learning system integrated with the Internet of Things (IoT) to predict the health insurance amount. IoT in healthcare enables interoperability, machine-to-machine communication, information exchange, and data movement that make healthcare service delivery effective. The model includes three phases (a) Feature Extraction, and (b) Weighted Feature Extraction, and (c) Prediction. The feature extraction process computes two statistical measures: First Order Statistics like mean, median, standard deviation, the maximum value of entire data, and minimum value of entire data, and Second-Order Statistics like Kurtosis, skewness, correlation, and entropy. The prediction process deploys a renowned machine learning algorithm called Neural Network (NN). As the main contribution, the weighted feature vector is developed here, where the weight optimally tuned by Modified Whale Optimization Algorithm (WOA). Also, the contribution relies on NN, where the training algorithm replaced with the same modified WOA for weight update. The modified WOA developed here is termed as Fitness dependent Randomized Whale Optimization Algorithm (FR-WOA). At last, the valuable experimental analysis using three datasets confirms the efficient performance of the suggested model. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Internet of things

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Quantum Kernel based Support Vector Machine with Quantum Approximate Optimization Algorithm Embedding for improved Lung Cancer Prediction 15

Quantum Kernel based Support Vector Machine with Quantum App...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Dutta, Shivanya Shomir Sandeep, Sahil Sridevi, S. Sridhar, Mohan Ram Singh, Chaitanya Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology Scope Chennai India

ISBN: (纸本)9798350370249

The integration of classical machine learning with quantum computing has sparked a remarkable revolution, opening avenues to tackle previously insurmountable challenges in diverse fields, particularly in clinical applications. One such challenging task is to predict lung cancer with minimal clinical samples and features. The prominent objective of this proposed research is to introduce a novel Quantum Kernel Support Vector Machine (QKSVM) constructed with the Quantum Kernel Circuit (QKC) enriched with Quantum Approximation Optimization Algorithm (QAOA) embedding circuit to classify highly complex lung cancer using a minimal number of samples and minimal feature dimensions. In this context, to compare the classification performance of the transformative potential of the proposed QAOA embedding QKSVM classifier model, we implemented two other variants of the QKC leveraging Angle embedding (Angle Embedding QKSVM) and Instantaneous Quantum Polynomial embedding (IQP Embedding QKSVM). We also compared their performance with that of a classical SVM classifier. In this study, we delve into the transformative potential classification performance of the proposed QAOA Embedding-based QKSVM classifier, which is contrasted particularly in scenarios with limited sample size and feature dimensions. Our investigation reveals the quantum kernel's superiority, even when operating with smaller datasets. This remarkable performance is attributed to the quantum models' robustness in extracting intricate diagnosing features, enabling it to extract more pertinent insights from fewer data points. Notably, the QKSVM model trained with a significantly selected feature vector of 4-Dimensions (4D) with Principal Component Analysis (PCA) emerges as the frontrunner in lung cancer predictions, achieving an outstanding test accuracy of 94.64% and the highest area under curve (AUC) metric of 82.33%. © 2024 IEEE.

关键词： Lung Cancer Prediction Quantum Approximate Optimization Algorithm(QAOA) embedding Quantum Kernel Support Vector Machine(QKSVM)

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A Privacy-Preserving Data Aggregation Protocol for Internet of Vehicles with Federated Learning

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Xu, Zisang Zhang, Ruirui Liang, Wei Li, Kuan-Ching Gu, Ke Li, Xiong Huang, Jialun Computer and Communication Engineer Institute Changsha University of Science and Technology Changsha China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Department of Computer Science and Information Engineering Providence University Taichung Taiwan Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Federated learning (FL) is widely used in various fields because it can guarantee the privacy of the original data source. However, in data-sensitive fields such as Internet of Vehicles (IoV), insecure communication channels, semi-trusted RoadSide Unit (RSU), and collusion between vehicles and the RSU may lead to leakage of model parameters. Moreover, when aggregating data, since different vehicles usually have different computing resources, vehicles with relatively insufficient computing resources will affect the data aggregation efficiency. Therefore, in order to solve the privacy leakage problem and improve the data aggregation efficiency, this paper proposes a privacy-preserving data aggregation protocol for IoV with FL. Firstly, the protocol is designed based on methods such as shamir secret sharing scheme, pallier homomorphic encryption scheme and blinding factor protection, which can guarantee the privacy of model parameters. Secondly, the protocol improves the data aggregation efficiency by setting dynamic training time windows. Thirdly, the protocol reduces the frequent participations of Trusted Authority (TA) by optimizing the fault-tolerance mechanism. Finally, the security analysis proves that the proposed protocol is secure, and the performance analysis results also show that the proposed protocol has high computation and communication efficiency. IEEE

关键词： Vehicles

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A Comprehensive Study on the Role of Artificial Intelligence in Animal Husbandry

A Comprehensive Study on the Role of Artificial Intelligence...

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2023 Intelligent Computing and Control for engineering and Business systems, ICCEBS 2023

作者： Surya Deveswar Reddy, Kovvuri Uday Shaik, Ayesha Balasundaram, A. Nivedita, M. Ram Kakarla, Lakshmi Sai Nithin, Mattapally Sai School of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India School of Computer Science and Engineering Vellore Institute of Technology Center for Cyber Physical Systems Chennai India

ISBN: (纸本)9798350394580

Distinguishing the breeds of animals is one of the major examination areas in animal welfare. Recognition of animal breeds demands numerous determining elements which are necessary to be explored and following classification of variety. Animals such as cows, buffaloes, sheep, goats, and other diverse creatures play a huge role in the production of milk, and sometimes meat. Every creature has different species and provide unique benefits which contribute a lot to the Indian economy. India is currently holding the first rank in milk export. Hence, it is compulsory to introduce automated methods that will differentiate breeds of various species. In addition, identifying diseases that effected to animals is also an important area related to animal protection and it is necessary to automate the detection as it would be effective for identify the diseases in primary stages. This paper carries out a full-scale study on how deep learning techniques are used in animal husbandry. © 2023 IEEE.

关键词： Animal Breeds Automate Methods Classification Deep learning Diseases Machine Learning

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Plant Diseases Detection Using Deep Learning and Machine Vision 3

Plant Diseases Detection Using Deep Learning and Machine Vis...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Singh, Nidhi School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9798350361186

This research delves into deep learning and machine vision applications for plant leaf disease detection in agricultural settings, focusing on farm village datasets. Utilizing a blend of authentic farm village data and synthetic data from Generative Adversarial Networks (GANs), three advanced convolutional neural network (CNN) models VGG16, ResNet50, and InceptionNet V3 are employed with transfer learning. Leveraging transfer learning enhances model performance through fine-tuning pre-trained networks. The study systematically evaluates models based on key metrics like accuracy, precision, recall, and F1 score. Results showcase the methodology's robustness, with ResNet50 emerging as the leading performer at 83.23%, contributing to precision agriculture's advancements with promising implications for sustainable farming and crop yield optimization. © 2024 IEEE.

关键词： Generative adversarial networks

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Application of Species Distribution Modelling in Agriculture: A Review 1

引用

International Conference on Data Analytics and Management, ICDAM 2023

作者： Agbezuge, Eric Yaw Balakrishnan, P. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (数字)9789819965472

ISBN: (纸本)9789819965465

Identification of the right abiotic factors for an ambient environment, soil, and water is critical for successful agricultural activities. However, these factors have been influenced by climate change, and other factors as such are no longer in their natural state of equilibrium. This has affected the ecosystem and has made previously suitable environments currently unsuitable for most organisms and crop production. To have production of healthy crops with high yield and quality, precise agricultural inputs are required, including the right environmental abiotic factors. This goal has been achieved to some extent via extensive studies carried out by various scholars to ascertain their impact and possible mitigating action. In this paper, we first investigated the factors that influence the environment adversely and the application of SDM as a solution depicted in other studies and further presented an overview of some of the SDM-based software packages and their applications, with much emphasis on agronomy. This review aims to provide insight into, how effective computer-based SDM has been or otherwise in resolving the challenges in the agriculture sector and the future direction for the application of SDM-based solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Climate change

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Parallelization of Molecular Dynamics Simulations Using Verlet Algorithm and OpenMP 1st

Parallelization of Molecular Dynamics Simulations Using Ver...

引用

1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Mathur, Preksha Azad, Hiteshwar Kumar Sangaraju, Sai Harsha Varma Agrawal, Ekansh School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9789819981281

Molecular Dynamics (MD) simulations provide qualitative insights into the dynamics of liquids, solids, and liquid–solid interfaces under varying temperature and pressure conditions. Accurate force calculations for ion cores are crucial for the success of classical MD simulations. This research explores the application of MD simulations to study large-scale systems and understand atom structure and interactions. MD simulations can successfully examine protein dynamics, such as folding and unfolding, contributing to a better understanding of their behavior. Integrating MD simulations with experimental data enables a holistic examination of atomic-level properties and their impact on cellular behavior. Additionally, MD simulations offer valuable insights into protein–ligand interactions and facilitate drug development. This study introduces an innovative optimization technique that uses the Verlet algorithm with OpenMP to parallelize MD simulations, resulting in significant computing time reductions for force and energy evaluations. This optimization methodology shows promise in various domains, including protein–ligand interactions and complex system investigations. The results show that, when compared to the serialization simulation, the proposed approach can deal with computational load balancing challenges better and more effectively, while also reducing computing time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Potential energy

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