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Decomposition-based hybrid methods employing statistical, machine learning, and deep learning models for crude oil price forecasting

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Neural Computing and Applications 2025年 1-46页

作者： Purohit, Sourav Kumar Panigrahi, Sibarama Department of Computer Science and Engineering Sambalpur University Institute of Information Technology Burla India Department of Computer Science and Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India

Crude oil prices (COP) profoundly influence global economic stability, with fluctuations reverberating across various sectors. Accurate forecasting of COP is indispensable for governments, policymakers, and stakeholders to make well-informed decisions and effectively mitigate risks. The decomposition-based hybrid models have been showing promising COP forecasting accuracy than other time series forecasting methods. Despite this fact, no systematic study has been conducted to evaluate the true potential of different decomposition-based hybrid methods employing different forecasting models to forecast the COP. Therefore, a hybrid modeling framework is developed by combining efficient decomposition techniques, namely empirical mode decomposition (EMD), ensemble EMD (EEMD), complete EEMD with adaptive noise (CEEMDAN), and variational mode decomposition (VMD) with seven statistical models, fourteen machine learning (ML) models, and six deep learning (DL) models. Further, a systematic study is conducted on the resulting decomposition-based hybrid models to find the best hybrid model for COP forecasting. Three distinct train-test data splits are employed to ensure a reliable evaluation of the models using four performance metrics. Extensive statistical analysis is conducted to identify the optimal combination of the decomposition technique and forecasting model for precise COP prediction. The results demonstrate that the proposed decomposition-based hybrid model employing VMD and Huber Regression is statistically the best method among all alternatives to forecast monthly COP. The proposed hybrid method VMD-Huber Regression improves the root mean square error (RMSE) by 21% than CEEMDAN-ARIMA, 58.31% than EEMD-Theta, 13.18% than EMD-Random Walk, and 49.44% than VMD-TBATS hybrid methods in 60–40 Train-Test split ratio. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Mean square error

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Malicious iOS Apps Detection Through Multi-Criteria Decision-Making Approach

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第1期49卷 207-220页

作者： Bhatt, Arpita Jadhav Sardana, Neetu Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Indonesia

In today’s era, smartphones are used in daily lives because they are ubiquitous and can be customized by installing third-party apps. As a result, the menaces because of these apps, which are potentially risky for user’s privacy, have increased. Information on smartphones is perhaps, more personal than compared to data stored on desktops or computers, making it an easy target for intruders. After Android, the most prevalently used mobile operating system is Apple’s iOS. Both Android and iOS follow permission-based access control to protect user’s privacy. However, the users are unaware whether the app is breaching the user’s privacy. To combat this problem, in the paper we propose a hybrid approach to detect malicious iOS apps based on its permissions. In the first phase, weights have been assigned to app permissions using multi-criteria decision-making (MCDM) approach namely Analytic Hierarchy Process (AHP), and in the second phase machine learning& ensemble learning techniques have been employed to train the classifiers for detecting malicious apps. To test the efficacy of the proposed method dataset comprising 1150 apps from 12 app categories has been used. The results demonstrate the proposed approach improves the efficacy of detecting malicious iOS apps for majority of categories. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Differential privacy

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Chaos Theory Based Gravitational Search Algorithm For Medical Image Segmentation 27th

Chaos Theory Based Gravitational Search Algorithm For Medica...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Rather, Sajad Ahmad Roy, Partha Pratim Das, Sujit Department of Computer Science and Engineering Indian Institute of Technology Roorkee247667 India Department of Computer Science and Engineering National Institute of Technology Telangana506004 India

ISBN: (纸本)9783031781032

Multilevel thresholding plays a crucial role in image processing, with extensive applications in object detection, machine vision, medical imaging, and traffic control systems. It entails the partitioning of an image into distinct regions based on optimal pixel values. However, as the number of threshold levels increases, so does the computational cost for segmentation. To address this challenge, a novel method is proposed namely Chaos theory based Gravitational Search Algorithm (CGSA) for multilevel thresholding. CGSA combines the standard Gravitational Search Algorithm (GSA) for exploration with chaotic maps for exploitation of the complex pixel problem space. In this study, Kapur’s entropy method is utilized to segment sample images into various partitions based on optimal pixel values. The effectiveness of CGSA in real-world scenarios is evaluated using COVID-19 chest CT scan imaging datasets from Kaggle database. The quality, symmetry, and consistency of the segmented output are assessed using metrics like Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM). Qualitative analysis includes convergence curves, segmented graphs, colormap images, and box plots. Statistical validation is conducted using the signed Wilcoxon rank sum test. Additionally, a comparison is made between CGSA’s performance and that of eight state-of-the-art heuristic algorithms. The findings demonstrate the superior performance of CGSA, evidenced by its reduced computational time and enhanced image quality metrics values. Specifically, CGSA achieved SSIM of 0.81, FSIM of 0.82, and PSNR of 24.27, surpassing the performance of other competitive algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Signal to noise ratio

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A New Variable-Speed Wind Energy Conversion System Using Double-Inverter-Fed Wound Rotor Induction Generator

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IEEE Transactions on Energy Conversion 2025年第1期40卷 347-359页

作者： Nanda, Anirban Hari, V. S. S. Pavan Kumar Indian Institute of Technology Bombay Department of Energy Science and Engineering Mumbai400076 India

The double-inverter-fed wound rotor induction generator (DI-WRIG) is proposed in this article as a potential candidate for wind energy conversion system (WECS) because of its increased freedom of control, wide speed range and efficient operation. By controlling the frequencies of the supplied voltages to both stator and rotor terminals of the generator, it is possible to operate the DI-WRIG in super-synchronous mode at any shaft speed, enabling efficient operation over the entire speed range. Further, the efficiency of a DI-WRIG can be maximized by appropriate selection of the total magnetizing current, the supply frequencies and the sharing of magnetizing current between stator and rotor, for a given turbine power curve. Design considerations of the proposed DI-WRIG for application in wind energy conversion are discussed. Moreover, an offline approach to derive optimum operating conditions of the generator for a given wind turbine characteristics is presented in this article. Experimental results are presented from a 7.5 kW laboratory-scale WECS prototype to demonstrate the performance of the proposed wind turbine generator. © 1986-2012 IEEE.

关键词： Stators

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Function Scheduling with Data Security in Serverless Computing Systems 17

Function Scheduling with Data Security in Serverless Computi...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Saha, Srestha Pandey, Arvind Addya, Sourav Kanti Brata Nath, Shubha Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India National Institute of Technology Karnataka Department of Computer Science and Engineering Surathkal India

ISBN: (纸本)9798331531195

In serverless computing, the service provider takes full responsibility for function management. However, serverless computing has many challenges regarding data security and function scheduling. To address these challenges, we have proposed a system to secure the data of an end-user. We also aim to meet the quality of service (QoS) for the end-user requests. This work presents a Simulated Annealing-based optimization algorithm for function placement. Also, we have Hyperledger Fabric, a blockchain framework in the system architecture for securing the data of an end-user. We have conducted experiments in Amazon Elastic Compute Cloud (EC2) taking virtual machine instances. The experiments in Amazon EC2 indicate that the proposed system secures the data and enhances the end-user's QoS. © 2025 IEEE.

关键词： Simulated annealing

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Deep Learning for Cognitive Task and Seizure Classification with Hilbert–Huang Transform and Variational Mode Decomposition 3rd

Deep Learning for Cognitive Task and Seizure Classification ...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Jain, Shraddha Srivastava, Rajeev Department of Computer Science and Engineering Indian Institute of Technology BHU Varanasi India

ISBN: (纸本)9789819743582

Electroencephalography (EEG) signals are often utilized to study cognitive processes and brain diseases. The non-stationary and non-linear nature of EEG signals makes their analysis difficult. A deep learning framework that suggested classifying seizures based on scalp EEG signals and automating cognitive tasks. We use a pre-processing module based on the Hilbert–Huang Transform (HHT) and Variational Mode Decomposition (VMD) to extract features from raw EEG data. We present an approach that combines deep learning with HHT and VMD for rapid and precise seizure detection. Our method detects seizures with an astounding 98.40% accuracy. Our suggested techniques have great potential for quantifying brain wave patterns and advancing neuroscience research, even outside of classification applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Variational mode decomposition

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Demonstration of an Application Layer Omni-frequency IoT Protocol 17

Demonstration of an Application Layer Omni-frequency IoT Pro...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Gupta, Nayancy Peddamalla, Lokesh Mendu, Veman Sai Prabhath Banda, Gourinath Indian Institute of Technology Indore Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798331531195

The advent of the Internet of Things (IoT) has transformed the way devices communicate, with an ever-increasing need for seamless interoperability and energy-efficient communication. This paper presents a unified omni-frequency protocol designed to operate independently of specific radio-wave protocols, allowing efficient and secure communication between users, devices (referred to as "things"), and cloud servers. The proposed protocol operates at the Application Layer and introduces two key components: the original equipment manufacturer (OEM) Server, which centrally manages user and device data, and the OEM Host, which verifies device credentials. This system enables efficient Internet Protocol(IP)-based and non-IP based communication, improving energy management and distribution of computational tasks to the edge. An Android application was developed to serve as the user interface, supporting multiple communication methods, including IP, Bluetooth classic version 5.0, Near-Field Communication (NFC) and ad hoc networking. The versatility of this unified approach optimizes the interactions, data processing, and energy utilization of IoT in various practical environments. © 2025 IEEE.

关键词： Network layers

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Sorted Range Selection and Range Minima Queries 2nd

Sorted Range Selection and Range Minima Queries

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2nd International Conference on Applied Algorithms, ICAA 2025

作者： Akram, Waseem Saxena, Sanjeev Department of Computer Science and Engineering Indian Institute of Technology Kanpur208016 India

ISBN: (纸本)9783031845420

Given an array A[1 : n] of n elements drawn from an ordered set, the sorted range selection problem is to build a data structure that can be used to answer following queries: Given a pair of indices i, j(1≤i≤j≤n), and a positive integer k, report the k smallest elements from the sub-array A[i : j] in sorted order. Brodal et al. (Brodal, G. S., Fagerberg, R., Greve, M., and López-Ortiz, A., Online sorted range reporting. Algorithms and Computation (2009) pp. 173–182) introduced the problem and gave a solution that can answer a query in O(k) time. The preprocessing takes O(nlogn) time and O(n) space. In this paper, we propose the only other possible optimal trade-off for the problem. After preprocessing using O(n) time and space, we can answer a range selection query in O(klogk) time. Moreover, the proposed algorithm reports the output elements individually in non-decreasing order. Our solution is simple and practical. We also study the problem in a dynamic setting. We also describe an extremely simple method for the range minima queries (most of whose parts are known), which takes almost (but not exactly) linear time. We believe that this method may be, in practice, faster and easier to implement in most cases. Moreover, we also give a simple dynamic solution to the range minimum problem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optimization

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Apple Leaf Disease Detection Using Transfer Learning

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Annals of Data science 2025年第1期12卷 213-222页

作者： Wani, Ozair Ahmad Zahoor, Umer Shah, Syed Zubair Ahmad Khan, Rijwan Department of Computer Science & Engineering Islamic University of Science and Technology Jammu & Kashmir Awantipora India Department of Computer Science and Engineering ABES Institute of Technology Uttar Pradesh Ghaziabad India

Automated detection of plant diseases is crucial as it simplifies the task of monitoring large farms and identifies diseases at their early stages to mitigate further plant degradation. Besides the decline in plant health, reduced production severely impacts the country’s economy. Traditional disease identification methods, relying on human experts, are slow, time-consuming, and impractical for large farms. Our proposed model utilizes a combination of pre-trained Resnet18, Alexnet, GoogLeNet, and VGG16 networks to classify apple tree leaves into categories such as healthy, black rot, apple cedar rust, and apple scab based on images. Various image enhancement techniques were employed to enhance the model’s accuracy. Ultimately, our model achieved an accuracy of 97.25% on the validation dataset, demonstrating excellent performance across various metrics. This suggests its potential for efficient and accurate plant health monitoring in the agricultural sector. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Adaptive pelican optimization with optimized mask RCNN for automatic lung cancer detection

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Multimedia Tools and Applications 2025年 1-21页

作者： Sudha, R. Maheswari, K.M. Uma Department of Computer Science and Engineering SRM Institute of Science and Technology Chengalpattu India Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu India

Lung cancer is a prevalent and deadly disease worldwide, necessitating accurate and timely detection methods for effective treatment. Deep learning-based approaches have emerged as promising solutions for automated medical image analysis. This study proposes an enhanced Mask R-CNN framework tailored specifically for the automatic detection and severity analysis of lung cancer from CT images. The proposed approach consists of three stages namely, pre-processing, lung nodule detection, and segmentation using enhanced Mask R-CNN and severity analysis. Our framework employs a deep convolutional neural network architecture trained on a comprehensive dataset of annotated lung images. By incorporating a region-based convolutional neural network (R-CNN) with a mask prediction branch, our model accurately localizes lung tumors while providing precise pixel-level segmentation masks. To enhance the performance of mask RCNN, the parameter present in the classifier is optimally selected using the adaptive pelican optimization (APO) algorithm. The proposed framework detects lung tumors and provides a comprehensive severity analysis, enabling clinicians to assess cancer stage and progression accurately. Evaluation on a benchmark dataset demonstrates superior detection accuracy and robustness compared to existing methods. Our enhanced Mask R-CNN approach shows promise as a valuable tool for early diagnosis and severity assessment of lung cancer, potentially improving patient outcomes and healthcare efficiency. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Lung cancer

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