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Data-Enhanced Prediction with Decomposition and Amplitude-Aware Permutation Entropy in Distributed Computing Systems

Data-Enhanced Prediction with Decomposition and Amplitude-Aw...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yuan, Haitao Hu, Qinglong Bi, Jing Zhang, Wei Zhang, Jia Zhou, MengChu School of Automation Science and Electrical Engineering Beihang University Beijing100191 China City University of Hong Kong Dept. of Computer Science 999077 Hong Kong College of Computer Beijing University of Technology Beijing100124 China University of Connecticut Dept. of Computer Science and Engineering StorrsCT06269 United States Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

In recent years, distributed computing has wit-nessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and inter-dependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVI for achieving high-accuracy workload prediction in distributed computing systems. SVI employs the Savitzky-Golay filter and variational mode decomposition for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVI improves the prediction accuracy by 35.4% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVI enhances the prediction accuracy of workload, CPU, and memory by 62.5%, 65.6%, and 69.1 %, respectively. © 2024 IEEE.

关键词： Quality of service

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Agro-smart Crop Recommendation and Yield Forecasting Employed through Machine Learning

Agro-smart Crop Recommendation and Yield Forecasting Employe...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Kollu, Praveen Kumar Raavi, Niranjani Duggineni, Dharani Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Computer Science and Engineering Andhra Pradesh Vijayawada India

ISBN: (纸本)9798350375442

In India, farmers often face significant challenges in selecting the appropriate crop variety for specific soil types, leading to substantial productivity losses. This project leverages precision agriculture by utilizing specific information about soil characteristics, location, and planting times to design a sophisticated model. The model employs Naïve Bayes for crop recommendation and Linear Regression for yield prediction. The model achieved a 99.54% accuracy in crop recommendation and an R2 of 0.94 in yield prediction, outperforming traditional methods. The implementation of high-end machine learning algorithms not only provides accurate crop recommendations but also predicts yields, equipping farmers with essential tools for informed decision- making to enhance productivity. The machine learning model is integrated into a user-friendly interface to facilitate its adoption. © 2024 IEEE.

关键词： Smart agriculture

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Artificial Intelligence to Enhance the Security of Internet of Things: A Comprehensive Review 3

Artificial Intelligence to Enhance the Security of Internet ...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dheekshaa, V.M. Harsha, V. Sindhu, M. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

The Internet of Things is the term we call when devices are connected to the network and work together to provide a better experience for users, facilitate better decision-making, and improve operations. As with any invention, this one also has its disadvantages. IoT devices have complex structures and low processing capacities. There are serious security risks in designing IoT like poor authentication, data leakage, and DDoS attacks. In this review paper, how AI can secure IoT is discussed. A few AI-driven strategies that help mask all these weaknesses are also discussed regarding intrusion detection systems (IDS), behavioural analytics, anomaly detection, and real-time threat monitoring. It presents a four-layered IoT architecture consisting of perception, network, support, and application layers. The use of the IoT systems will become more flexible and efficient in data security and collection, secure and encrypted communication, and predictive security analytics. AI will play the same role in improving the threat perception, real-time identification, and response, as this will make it indispensable to ensure the IoT environment security. This work emphasizes the urgent need for solid AI-powered, multilayered adaptive security frameworks to match the evolving demands of cybersecurity. © 2025 IEEE.

关键词： Network intrusion

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Real-Time Traffic Prediction And Optimization using Machine Learning 3

Real-Time Traffic Prediction And Optimization using Machine ...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vineetha, Kundrapu Tharun Reddy, M. Jaffer Ali, Md. Prathima, Narisetty Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The Real-Time Traffic Prediction and Optimization System may be described as enriched, spacious, algorithm-oriented, and designed to help contribute to the anti-symptomatic deprievement of increasingly congested urban traffic streams. The system with the help of integrating real time data of numerous sources like GPS tracker, traffic sensor as well as weather API, the system using advanced machine learning algorithm in order to predict traffic accurately and optimize routes based on it. This predictive capability then greatly improves the flow of vehicles, the time taken, and the occurrence of choke points. The structure of the system is fully based in the cloud, which makes it easy to scale and very compatible with other systems to amalgamate a vast array of data inputs with high dependability. Fully customizable and intuitive control panel gives live view of traffic, offers actionable stats, and instant control mechanisms to traffic operators to make correct decisions quickly. Congestion in urban areas has an influence on time taken to cover a certain distance, fuel consumption and emission to the atmosphere. On the basis of the above analysis, this work extends a Real-Time Traffic Prediction and Optimization System (RTPOPS) using machine learning approach to predict the traffic density and to further optimize traffic signals and routes. The used system demonstrated successful management and reduction of vehicle density by 30%, and increasing speed limits by 18% and proved that Intelligent Urban Mobility has the potential to be achieved. © 2025 IEEE.

关键词： Traffic congestion

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Smart Shopping Cart using CNN 14

Smart Shopping Cart using CNN

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

作者： Vidhya, S.G. Dhruva, M.S. Deekshitha, H.C. Kshama, S. Tejaswini, K.R. Dept. of Information Science and Engineering BGSIT India Dept. of Computer Science and Engineering BGSIT India

According to present scenario, now a days it is often observed during shopping groceries, vegetables, fruits and other needs in malls, markets, bazaars etc. There is a huge rush, billing of the cart products will be delayed due to more number of customers standing in queues at every bill counter especially on holidays and weekends. This leads to loss of valuable time of customers, To overcome these kind of situations, a simple solution is introduced that is "Smart Shopping Cart" using Convolution Neural Network (CNN). This smart cart is connected to cameras that are connected to webpage through hot spot which acts as a sensor, which sense the image of the products and process that image, the model processes the image only if the product is trained, tested and stored in the database. After processing the product image it identifies the product and add the product to the bill. To add the product to the bill we have used a high level pre-trained API known as Tensor flow which acts as a communication link between the sensor and webpage. If the product is removed from the cart then the product will be removed from the bill list. By implementing this model we can save the time of both seller and consumer and avoid misuse of billings. © Grenze Scientific Society, 2023.

关键词： Websites

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Enhanced Image Captioning Using CNN and BLIP Models

Enhanced Image Captioning Using CNN and BLIP Models

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Tejesh, Vambara Supriya, M. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331509828

This research focuses on generating image captions using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. As deep learning advances, the availability of large datasets and increased computing power make it more feasible to build models capable of creating captions for images. This paper uses CNN and RNN models in Python to achieve this. Image captioning combines image recognition and Natural Language Processing (NLP) to interpret image context and express it in English, drawing on core computer vision principles. This study reviews important concepts in image captioning, including the applications of Keras, NumPy, and Jupyter notebook for development. This paper also explores the use of the Flickr dataset and CNN for image classification. The BLEU score of the proposed models is found to be close to 60%. On further enhancement to the model, it could be achieved to get a better score. © 2025 IEEE.

关键词： Convolutional neural networks

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A Novel Machine Learning-Based Early Warning Detection System for Business Customer Churn 5

A Novel Machine Learning-Based Early Warning Detection Syste...

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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Chandan, Radha Raman Ramaraj, S. Sarishma Praba Devi, P. Sj, Sultanuddin Indhuma, E. Department of Computer Science UP Varanasi221011 India Karpagam College of Engineering Department of Computer Science and Engineering TamilNadu Coimbatore India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Sona College of Technology Department of Management Studies Salem India Dhanalakshmi College of Engineering Dept of Cyber Security Tambaram Manimangalam Chennai India St. Martins Engineering College Department of Computer Science and Engineering Telangana Secunderabad India

ISBN: (纸本)9798350351378

This study presents an Early Warning Detection System (ML-EWDS) based on machine learning for reducing commercial customer turnover. Companies in all sectors need to be especially concerned about client churn in today's environment of volatile markets and plenty of consumer options. The ML-EWDS overcomes this difficulty by analyzing past customer data that has been augmented with Regency, Frequency, and Monetary (RFM) attributes using an ensemble learning model that includes a Random Forest classifier. The system's high accuracy (87.5%) and ROC-AUC (0.92) are the result of meticulous data preparation, model training, and cross-validation. Next, the ML-EWDS is put into action in a live business setting, where it alerts potentially churned consumers ahead of time. The solution significantly reduces turnover by 70% during a three-month assessment period, which has a beneficial effect on income. The ethical implementation of this technology is emphasized, with a focus on data protection and bias reduction. This study demonstrates the revolutionary potential of ML-EWDS in CRM, with implications for both academics and business and many possible avenues for further development. © 2024 IEEE.

关键词： Learning systems

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Multi Layered Defense Architecture for DDoS Protection in Private Cloud Environments 2

Multi Layered Defense Architecture for DDoS Protection in Pr...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Singh, Shashwat Sharma, Anubha Soni, Shani Prakash, Ayushi Ajay Kumar Garg Engineering College Dept. of Computer Science and Engineering Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350368109

Private cloud solutions have increasingly been adopted because of the demand from organizations for enhanced data security, control over regulatory compliance, and IT infrastructure has been rising. Although public cloud services offer built-in security solutions, private clouds must possess a custom built architectures to address critical challenges, including Distributed Denial of Service (DDoS) attacks, among *** are often deployed behind private cloud environments without the benefits of prebuilt SaaS based DDoS protection. In this exposition, we present an architecture for multilayer protection against DDoS attacks in private cloud environments, which includes the following key components: Global Load Balancer, Traffic Scrubbing Center, Signature based IDPS, and Command and Control Center. Further, we also present a custom Command and Control Center, which utilizes CNNs for runtime handling of network traffic flow, based on real-time monitoring and activity patterns, to mitigate the menace of DDoS. Experimental results show that the architecture is efficient in filtering volumetric and application layer DDoS attacks, ensuring a malicious packet drop rate greater than 95%, while maintaining latency below 150 ms. © 2024 IEEE.

关键词： Cloud security

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Dynamic Landslide Prediction, Monitoring, and Early Warning with Explainable AI: A Comprehensive Approach 3

Dynamic Landslide Prediction, Monitoring, and Early Warning ...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Binu, Kristin Elizabeth Anoopkumar, Lakshmi Thampi Sunil, Maria Jose, Merin Preetha, K.G. Rajagiri School of Engineering & Technology Dept. of Computer Science and Engineering Rajagiri Valley PO Kochi India

ISBN: (纸本)9798350375190

Landslides are a major threat to infrastructure, human lives, and the environment. Therefore, early warning systems and precise forecasting are essential for reducing their effects. This study investigates how to enhance landslide prediction models using Explainable Artificial Intelligence (XAI) methodologies. Intending to improve complex machine learning models' interpretability and transparency, XAI seeks to improve comprehension and confidence in the model's predictions. XAI methodologies used are SHapley Additive exPlanations (SHAP), LIME (Local Interpretable Model-agnostic Explanations), and feature importance analysis. These techniques produce comprehensible explanations for each forecast and assist in determining the most significant variables in landslide prediction. The proposed XAI-enhanced landslide prediction model demonstrates promising results in terms of accuracy and interpretability. The research reported here helps to design more potent early warning systems for landslides by advancing explainable AI applications in geoscience and disaster risk reduction. © 2024 IEEE.

关键词： Landslides

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Evaluation and Validation of Design Metrics in Machine Learning for Depression Detection 18

Evaluation and Validation of Design Metrics in Machine Learn...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Ajmal, Sahar Usman, Muhammad Javed, Abqa National University of Computer and Emerging Sciences Dept. of Software Engineering Chiniot Faisalabad Pakistan National University of Computer and Emerging Sciences Dept. of Computer Science Chiniot Faisalabad Pakistan Univ. of Engineering and Technology Dept. of Computer Science Lahore Pakistan

ISBN: (纸本)9798350328172

Depression is a medical syndrome that is often neglected and considered not as significant as physical illness due to lack of awareness and knowledge, but it should be treated in early stages to avoid extreme cases such as suicide. Therefore, Machine Learning Algorithms (MLA) are used to detect depression primitively to avoid extreme hazards. In-ternational Organization for Standardization (ISO) standards are essential for designing metrics in MLA as they ensure global consistency, quality assurance, and objective benchmark, enhancing credibility and reliability of the model. Although there is no proper standard design level quality metric is defined for MLA. Therefore, in this study, ISO 9126 standard is used to make as a quality standard considering functionality parameter of MLA design. These metrics are used to evaluate at the design level of MLA such as evaluation of dataset, preprocessing, MLA, and results. We have provide comparative analysis of MLA with partial functionality compliance with full ISO functionality conformance. In this study, MLA with ISO have significance compliance which is promising, where the raw data added to MLA to get the output and or the other hand all the steps are performed. The results clearly show that proposed model of MLA design metrics are out-performed provides 28% better results in terms of evaluations. Furthermore, the proposed design quality metrics can be used as the standard for the MLA domain. © 2023 IEEE.

关键词： Quality assurance

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