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From light sensing to adaptive learning:hafnium diselenide reconfigurable memcapacitive devices in neuromorphic computing

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Light(science & Applications) 2025年第1期14卷 215-226页

作者： Bashayr Alqahtani Hanrui Li Abdul Momin Syed Nazek El-Atab Electrical and Computer Engineering Program Computer Electrical Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)ThuwalSaudi Arabia Electrical Engineering Department College of EngineeringPrincess Nourah Bint Abdulrahman University(PNU)RiyadhSaudi Arabia

Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior,dynamic responses,and energy efficiency *** charge-based or emerging memory technologies such as memristors have been developed to emulate synaptic plasticity,replicating the key functionality of neurons—integrating diverse presynaptic inputs to fire electrical impulses—has remained *** this study,we developed reconfigurable metal-oxide-semiconductor capacitors(MOSCaps)based on hafnium diselenide(HfSe2).The proposed devices exhibit(1)optoelectronic synaptic features and perform separate stimulus-associated learning,indicating considerable adaptive neuron emulation,(2)dual light-enabled charge-trapping and memcapacitive behavior within the same MOSCap device,whose threshold voltage and capacitance vary based on the light intensity across the visible spectrum,(3)memcapacitor volatility tuning based on the biasing conditions,enabling the transition from volatile light sensing to non-volatile optical data *** reconfigurability and multifunctionality of MOSCap were used to integrate the device into a leaky integrate-and-fire neuron model within a spiking neural network to dynamically adjust firing patterns based on light stimuli and detect exoplanets through variations in light intensity.

关键词： integrate volatile integrating

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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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AmplitudeArrow: On-the-Go AR Menu Selection Using Consecutive Simple Head Gestures and Amplitude Visualization

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IEEE Transactions on Visualization and computer Graphics 2025年第05期31卷 3408-3417页

作者： Tian, Yang Zhang, Youpeng Yan, Yukang Zhao, Shengdong Ma, Xiaojuan Shi, Yuanchun Guangxi University Department of Computer Science Guangxi Key Laboratory of Multimedia Communications and Network Technology China University of Rochester Department of Computer Science United States City University of Hong Kong School of Creative Media and the Department of Computer Science Hong Kong Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Tsinghua University Department of Computer Science and Technology China

Heads-up computing aims to provide synergistic digital assistance that minimally interferes with users' on-the-go daily activities. Currently, the input modalities of heads-up computing are mainly voice and finger gestures. In this work, we propose and evaluate the AmplitudeArrow (AA) technique designed for on-the-go AR menu selection to demonstrate that consecutive simple head gestures can also be an effective input modality for heads-up computing. Specifically, AA arranges menu icons into one/two row(s). To select a target icon, the user first makes their head yaw to pre-select the target icon or the column containing it and then makes their head pitch to make the arrow in the target icon expand until the arrow covers the target icon completely, i.e., the pitch amplitude surpasses the selection confirmation threshold. User studies indicated that AA demonstrated robust resistance to walking-caused head perturbation and external factors such as other people/obstacles, delivering high accuracy (error rate © 1995-2012 IEEE.

关键词： Augmented reality

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A systematic literature review of video forgery detection techniques

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Multimedia Tools and Applications 2025年 1-51页

作者： Aulakh, Manpreet Kaur Kanwal, Navdeep Bansal, Manish Department of Computer Science and Engineering Punjabi University Patiala Punjab Patiala India Department of Computer Science Baba Farid Group of Institutions Punjab Bathinda India

In the last decade, due to the widespread and inexpensive availability of digital video cameras, digital videos (DV) are employed for security purposes daily, and they are generally regarded as a more credible form of evidence than still photographs. Due to the tremendous growth of video editing tools, anyone with access to advanced editing software and a modern Smartphone can easily do digital video manipulations and fake it. As a result, to utilize video content as proof in court, it is necessary to evaluate and determine whether it is original or modified. To check the integrity and validity of video recordings, digital forgery detection techniques are required. The objective of the study is to present a systematic review of techniques for detecting forgery in digital videos. We conducted a systematic literature review (SLR) in this study to present a detailed review of the initial and recent research efforts in Digital video forgery detection, summarizing 260 relevant papers from 2000 to 2023 that have presented a variety of techniques. For analysis, we have presented our references in three different ways: according to the type of forgery detected, according to the type of model or technique used and according to the feature used for forgery detection. We look through the several datasets that are cited in articles and determine their applicable domain. Then, we looked at the numerous measuring metrics employed by different research papers and compared the effectiveness of deep and non-deep models in each category of forgery that was found. Finally, research gaps concerning passive video forgery detection are classified and highlighted. A comparison between our survey and other existing survey articles has been presented in the paper. Researchers who wish to work on video forgery detection will get assistance to determine what kind of efforts in forgery detection work is still required. This survey will also help to select techniques and features based on their

关键词： Video recording

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Ensemble Machine Learning-Based Approach to Predict Cervical Cancer with Hyperparameter Tuning and Model Explainability

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Biomedical Materials and Devices 2025年 1-28页

作者： Uddin, Khandaker Mohammad Mohi Bhuiyan, Md. Tofael Ahmed Saad, Mirza Nadim Islam, Ashfaqul Islam, Md. Manowarul Department of Computer Science and Engineering Southeast University Dhaka 1208 China Department of Computer Science and Engineering University of Dhaka Dhaka Bangladesh Department of Computer Science and Engineering Jagannath University Dhaka India

Cervical cancer remains the top killer of women at a young age in the world, 85% of cases are detected in low-income countries. Preventive measures and therapeutic response are enhanced if potential hazards are identified early. This research belongs to this field by introducing an end-to-end prediction model based on individual medical records and early screening data thus emphasizing the discovery of meaningful predictors. In order to overcome issues with feature selection and class imbalances, our study creates an ensemble prediction framework that blends Random Forest and Logistic Regression techniques. In addition to achieving an astounding accuracy of 99.75%, the model guarantees transparency in its decision-making processes by utilizing sophisticated machine learning algorithms in conjunction with interpretability tools like SHAP and LIME, which is essential for applications in healthcare. The creation of an extensive ensemble method that combines several machine learning classifiers, advanced feature selection techniques for locating important predictive factors, and interpretability techniques to help healthcare professionals better understand complex predictions are some of the research’s main investments. By offering accurate and comprehensible risk assessments, this novel method has the potential to revolutionize clinical decision-making and enhance early cancer of the cervical cavity identification. This research promotes the development of more proactive and individualized cancer screening methods by fusing cutting-edge computational technology with medical diagnostics, improving health outcomes everywhere. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Cervical cancer Explainable machine learning Hybrid model introduction Hyperparameter tuning feature selection Machine learning Stacking ensemble technique

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Deep Learning-Based Image Watermarking Using Catalan Transform and Non-Negative Matrix Factorization

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IEEE Access 2025年 13卷 68995-69020页

作者： Fahim Hossain Saikat, Md. Provath, Md. Al-Mamun Deb, Kaushik Dhar, Pranab Kumar Shimamura, Tetsuya Department of Computer Science and Engineering Chittagong4349 Bangladesh Saitama University Department of Information and Computer Sciences Saitama338-8570 Japan

In the digital age, embedding imperceptible data into images for authenticating ownership and content integrity through watermarking has gained immense importance. Existing watermarking methods struggle to balance imperceptibility, robustness, and security, resulting in suboptimal peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), normalized correlation (NC) values, along with higher time complexity. To overcome these constraints, this study proposes a new method for image watermarking that uses deep learning and operates within the YIQ color space. The main contribution of the proposed method is its new combination of the Catalan transform (CT) and non-negative matrix factorization (NMF) with an artificial neural network (ANN) for enhanced watermark embedding and extraction. CT enhances pixel intensity while NMF extracts the most significant features from each (m x m) block (with m= 2 in simulations), improving the method's robustness and imperceptibility. Before embedding, the watermark undergoes a new scrambling technique combining adaptive diffusion and non-linear transform, significantly enhancing security. A new embedding equation integrates the watermark into the non-negative elements of the host image blocks. Statistical features from watermarked blocks are used to create a dataset for extraction, and an ANN model trained on this dataset enables accurate watermark recovery. The proposed method achieves a PSNR of 66.43 to 77.02 dB (31.91% improvement), SSIM of 0.99 to 1.00 (1.02% improvement), and NC of 0.99 to 1.00 (1.88% improvement). Its computational complexity is O(n2), a 33.33% reduction compared to traditional O(n3) methods. Security metrics include RE = 0.3094, UACI = 34.21%, and NPCR = 99.15%, demonstrating superior robustness and efficiency over existing approaches. © 2013 IEEE.

关键词： Non-negative matrix factorization

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An effective intrusion detection system based on feature selection and regularized long short-term memory classifier

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Multimedia Tools and Applications 2025年 1-24页

作者： Chikkalwar, Sudha Rani Garapati, Yugandhar Department of Computer science and Engineering GITAM Deemed to be University Rudraram Hyderabad India Department of Computer science and Informatics Mahatma Gandhi University Telangana Nalgonda India

In recent days, the expansion of Internet of Things (IoT) and the quick advancement of computer system applications contribute to the current phenomenon of data growth. The field of intrusion detection has expanded considerably as a result of the need to protect networks from various attacks. Previously, security has been provided by combining conventional intrusion monitoring systems with a high number of security measures. However, the ability of combined systems to analyze enormous amounts of data has been restricted. Therefore, in this research, open-source and freely available datasets known as NSL-KDD and CSE-CIC-IDS2018 datasets are analyzed. Those datasets contain a class column that splits the attack detection into two classes (normal and anomalous) and four categories (DoS, probe, R2L, and U2R). Then, feature encoding and scaling are used for pre-processing. After that, a SelectPercentile-based feature selection algorithm is suggested to identify the numerous various intrusion attacks and choose the best features. Later, the Regularized Long Short-Term Memory (LSTM) is employed to classify the data. The result analysis verified that Regularized LSTM surpasses the existing models in the NSL-KDD based on accuracy (99.95%), false alarm rate (0.05%), attack detection rate (99.82%), and f1-measure (99.94%). While in the CSE-CIC-IDS2018 dataset, the Regularized LSTM method achieves better accuracy (99.33%), false alarm rate (0.07%), attack detection rate (99.96), and f1-measure (99.95%). © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Network intrusion

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Making Sense of Big Data in Intelligent Transportation Systems: Current Trends, Challenges and Future Directions

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ACM Computing Surveys 2025年第8期57卷 1-43页

作者： Ahmad Jan, Mian Adil, Muhammad Brik, Bouziane Harous, Saad Abbas, Sohail Department of Computer Science University of Sharjah Sharjah27272 United Arab Emirates Department of Computer Science and Engineering University at Buffalo BuffaloNY United States

Intelligent Transportation Systems (ITS) generate massive amounts of Big Data through both sensory and non-sensory platforms. The data support batch processing as well as stream processing, which are essential for reliable operations on the roads and connected vehicles in ITS. Despite the immense potential of Big Data intelligence in ITS, autonomous vehicles are largely confined to testing and trial phases. The research community is working tirelessly to improve the reliability of ITS by designing new protocols, standards, and connectivity paradigms. In the recent past, several surveys have been conducted that focus on Big Data Intelligence for ITS, yet none of them have comprehensively addressed the fundamental challenges hindering the widespread adoption of autonomous vehicles on the roads. Our survey aims to help readers better understand the technological advancements by delving deep into Big Data architecture, focusing on data acquisition, data storage, and data visualization. We reviewed sensory and non-sensory platforms for data acquisition, data storage repositories for archival and retrieval of large datasets, and data visualization for presenting the processed data in an interactive and comprehensible format. To this end, we discussed the current research progress by comprehensively covering the literature and highlighting challenges that urgently require the attention of the research community. Based on the concluding remarks, we argued that these challenges hinder the widespread presence of autonomous vehicles on the roads. Understanding these challenges is important for a more informed discussion on the future of self-driven technology. Moreover, we acknowledge that these challenges not only affect individual layers but also impact the functionality of subsequent layers. Finally, we outline our future work that explores how resolving these challenges could enable the realization of innovations such as smart charging systems on the roads and data centers

关键词： Intelligent transportation systems big data sensors storage visualization data centers on the wheels smart computers on the roads

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Enhancing cyber threat detection with an improved artificial neural network model

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Data science and Management 2025年第1期8卷 107-115页

作者： Oyinloye, Toluwase Sunday Arowolo, Micheal Olaolu Prasad, Rajesh Department of Computer Science African University of Science and Technology Abuja900107 Nigeria Department of Electrical Engineering and Computer Science University of Missouri ColumbiaMO65201 United States

Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems (IDS). Data labeling difficulties, incorrect conclusions, and vulnerability to malicious data injections are only a few drawbacks of using machine learning algorithms for cybersecurity. To overcome these obstacles, researchers have created several network IDS models, such as the Hidden Naive Bayes Multiclass Classifier and supervised/unsupervised machine learning techniques. This study provides an updated learning strategy for artificial neural network (ANN) to address data categorization problems caused by unbalanced data. Compared to traditional approaches, the augmented ANN's 92% accuracy is a significant improvement owing to the network's increased resilience to disturbances and computational complexity, brought about by the addition of a random weight and standard scaler. Considering the ever-evolving nature of cybersecurity threats, this study introduces a revolutionary intrusion detection method. © 2024 Xi'an Jiaotong University

关键词： Network intrusion

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Enhancing English accent identification in automatic speech recognition using spectral features and hybrid CNN-BiLSTM model

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Multimedia Tools and Applications 2025年 1-28页

作者： Ahmed, Ghayas Lawaye, Aadil Ahmad Jain, Vishal Chatterjee, Jyotir Moy Mahajan, Shubham Department of Computer Sciences Baba Ghulam Shah Badshah University J&K Rajouri India Department of Computer Science & Engineering Sharda University Noida India Dehradun India Department of Computer Science & Engineering Amity University Haryana India

Automatic Speech Recognition (ASR) has been the regnant research area in the domain of Natural Language Processing for the last few decades. Past years’ advancement provides progress in this area of research. The accent of spoken is the prominent factor affecting the performance of ASR. Accent is the pronunciation of a word in a distinct way which may vary from specimen to specimen depending upon their social class, formality, age, vocal property, gender, geography, and influence of native or other language. Recognizing an accent requires various complex characteristics and features of voice such as voice quality, prosody, and phoneme pronunciation which are difficult to extract and analyze. To solve these difficulties, the researchers focus their attention on spectral representation such as Mel Spectrogram and Mel-Frequency Cepstral Coefficient (MFCC). In this study, we will analyze which features achieve maximum accuracy for the English accent classification task. Here in this work, we perform our experiments on Log Mel filter bank and MFCC features with four different window function. We extract features from raw audio with various windowing function and train a hybrid model of Convolutional Neural Network and Bidirectional Long Short-term Memory network (CNN-BiLSTM) on a custom dataset having nine different accents of English language namely American, Australian, British, Indian (Oriya, Bangla, Telegu, Malayalam), and Welsh. The accuracy of each feature is evaluated and compared. The log Mel filter bank outputs the highest accuracy of 99.75% and 99.91% of training and validation with Han window function while MFCC with Bartlett window function achieves 99.98% of training and 99.33% of validation accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Spectrographs

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