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Enhancing patient-independent detection of freezing of gait in Parkinson’s disease with deep adversarial network

Neural Computing and Applications 2025年 1-21页

作者： Fahad, Md Shah Ranjan, Ashish Kumar, Gautam Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi India Department of Computer Science and Engineering C. V. Raman Global University Odisha Bhubaneswar India Department of Computer Science and Engineering National Institute of Technology Delhi Delhi India

Freezing of gait (FoG) refers to sudden, relatively brief episodes of gait arrest in Parkinson’s disease, known to manifest in the advanced stages of the condition. Events of freezing are associated with tumbles, traumas, and psychological repercussions, significantly impacting the patient’s quality of life. The use of accelerometer data derived from sensors put on a patient’s body to detect FoG has previously been proposed using convolutional neural network (CNN)-based deep learning algorithms. Here, we combine a CNN + Long short-term Memory (LSTM)-attention model to detect FoG episodes—a first for the detection of FoG using the accelerometer data. CNN facilitates automatic feature extraction from the accelerometer data itself. The output from the CNN is fed to the LSTM network, which is known for capturing sequential information. Further, the attention mechanism introduces relative focus on individual sub-sequences during the training of the LSTM network. The proposed model is made patient independent using adversarial training. The proposed model achieved improvements of +7.60% (without adversarial training) and + 8.36% (with adversarial training) in the sensitivity values over state of the art. This is obtained with little or no compromise in specificity values. The corresponding improvements in accuracy values are +3.81 and +2.23%, respectively. These findings imply that the proposed method can detect FoG gaits satisfactorily and can be effective in achieving accurate monitoring and gait assistance for Parkinson’s disease patients during daily life and rehabilitation therapy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Accelerometers

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Enhancing Voice Phishing Detection Using Multilingual Back-Translation and SMOTE: An Empirical Study

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IEEE Access 2025年 13卷 37946-37965页

作者： Boussougou, Milandu Keith Moussavou Hamandawana, Prince Park, Dong-Joo Soongsil University Department of Computer Science and Engineering Seoul06978 Korea Republic of Ajou University Department of Software and Computer Engineering Suwon16499 Korea Republic of Soongsil University School of Computer Science and Engineering Seoul06978 Korea Republic of

With the widespread global trend of voice phishing or vishing attacks, the development of effective detection models using artificial intelligence (AI) has been hindered by the lack of high-quality and large volumes of data. This lack of data reflecting a real vishing scenario often leads to imbalanced datasets and biased detection models. Therefore, we present in this paper a data augmentation (DA) method for expanding the imbalanced Korean call content vishing (KorCCVi) dataset to address the existing data asymmetry problem and enhance the performance of Korean vishing detection. The proposed approach for DA involves using the back-translation (BT) method with three different intermediate languages: English, Chinese, and Japanese. The proposed method offers several advantages over the traditional synthetic minority oversampling technique (SMOTE), which is the main technique used to compare with our multilingual BT approach. Using these two DA techniques, several machine learning (ML) and deep learning (DL) models were trained on the original imbalanced dataset, the dataset balanced with SMOTE and its variants, and the dataset augmented with our method. We analyzed the impact of these DA methods on the performance of the models, demonstrated the benefits of each approach, and suggested the most suitable approach. The performance of the trained models was evaluated using the accuracy, precision, recall, and F1-score metrics. The experimental results demonstrated that the proposed multilingual BT method effectively expands the dataset while preserving its contextual and linguistic characteristics. The average performance of the models revealed that those trained on the augmented dataset outperformed the other models. They achieved F1-scores of 98.91% for the back-translated data, 98.14% for the original data, and 97.23% for SMOTE. © 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecomm

关键词： Phishing

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Isomorphic Classification of First Degree Cellular Automata

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Complex Systems 2025年第1期34卷 59-90页

作者： Vikrant, Vicky Bhattacharjee, Kamalika Mukherjee, Sukanya Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirappalli620015 India Department of Computer Science and Engineering Institute of Engineering and Management West Bengal Kolkata700091 India

This paper explores the concept of isomorphism in cellular automata (CAs), focusing on identifying and understanding isomorphic relationships between distinct CAs. A cellular automaton (CA) is said to be isomorphic to another CA if the configuration transition diagrams of the two CAs are identical. To illustrate how isomorphism entails the preservation of structural properties while allowing for variations in state representations, we exhaustively explore the isomorphic reversible rules in the first degree CAs (FDCAs), that is, the simple three-neighborhood CAs represented by eight parameters. The experimental study includes reversible FDCAs for states two to 10 with cell lengths of four to seven under the null boundary condition and states two to seven with cell lengths of four to seven under the periodic boundary condition. Given a CA, we also provide two synthesis methods for identifying the set of CAs that are isomorphic to it. The first method operates by permuting the states of a group of CA cell locations, whereas the second method involves exchanging cells at various points. We demonstrate that, depending on the range of exchanged cell positions, the isomorphic CAs produced in this way may have a bigger neighborhood than the input CA. Finally, we provide an exhaustive classification of the elementary CAs (ECAs) using the first synthesis method and compare it with existing results. © 2025, Complex Systems Publications, Inc. All rights reserved.

关键词： classification cycle structure FDCA first degree cellular automaton parameters reversibility synthesis schemes

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Performance optimization in ddos prediction with ensemble based approach

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Multimedia Tools and Applications 2025年第12期84卷 10631-10644页

作者： Dogra, Amit Taqdir Department of Computer Science and Engineering SoET BGSB University Rajouri India Department of Computer Science Engineering and Technology Guru Nanak Dev University Regional Campus Gurdaspur India

Distributed Denial of Service (DDoS) attacks pose a significant threat to network infrastructures, leading to service disruptions and potential financial losses. In this study, we propose an ensemble-based approach for DDoS attack detection, leveraging the strengths of three different classifiers: Adaboost, K-Nearest Neighbors (KNN), and Random Forest. We apply data normalization during pre-processing, utilize a Multi-Layer Perceptron (MLP) for feature extraction, and combine the classifiers using an ensemble approach. The performance of each individual classifier and the ensemble is extensively evaluated, and the results demonstrate the effectiveness of the proposed ensemble in accurately identifying and mitigating DDoS attacks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Denial-of-service attack

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Cyber-attack detection based on a deep chaotic invasive weed kernel optimized machine learning classifier in cloud computing

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Soft Computing 2025年第4期29卷 2015-2030页

作者： Indrasena Reddy, M. Siva Kumar, A.P. Subba Reddy, K. Department of Computer Science and Engineering BVRIT Hyderabad College of Engineering for Women Hyderabad500090 India Department of Computer Science and Engineering Jawaharlal Nehru Technological University Ananthapuramu Andhra Pradesh Ananthapuramu India Department of Computer Science and Engineering Rajeev Gandhi Memorial College of Engineering and Technology Andhra Pradesh Nandyala India

Nowadays, Cloud Computing has attracted a lot of interest from both individual users and organization. However, cloud computing applications face certain security issues, such as data integrity, user privacy, and service availability. The only better solution to such issues is to identify and prevent cyber threats before they can cause seriously harm the cloud computing system. Several methods have been suggested so far to detect the attack in cloud computing (CC), but no one method attains satisfactory results. To overwhelm these drawbacks, a Cyber Security Attack Detection using Deep Kernel Machine Learning optimized with Chaotic Invasive Weed Optimization algorithm is proposed in this paper for Cloud Computing. Here, the data is amassed from CSE-CIC-IDS2018 and Bot-IoT datasets. Subsequently, a pre-processing step involving redundancy reduction and missing value replacement for uncertainty removal is achieved through the Developed Random Forest with Local Least Squares (DRFLLS) method. By utilizing Entropy-Kurtosis based feature selection approach, the pre-processing data is given to the feature selection to identify the optimal features. The selected features are supplied to the Deep Kernel Machine Learning classifier (DK-ML), which is optimized using the Chaotic Invasive Weed Optimization algorithm (Chaotic-IWOA) for classification. This classification process classifies the data as normal or anomalous categories with anomalies encompassing, like DoS, DDoS, Theft attacks, and normal attacks. The proposed technique is activated in MATLAB. The proposed technique achieves 24.88%, 17.98%, 45.65%, 35.95% better accuracy for CSE-CIC-IDS2018 dataset, and 23.93%, 13.94%, 32.94%, and 29.04% better accuracy for Bot-IoT dataset. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Cyber attacks

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A Secret Sharing-Inspired Robust Distributed Backdoor Attack to Federated Learning

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ACM Transactions on Privacy and Security 2025年第2期28卷 1-19页

作者： Yang, Yuxin Li, Qiang Ji, Yuede Wang, Binghui College of Computer Science and Technology Jilin University Changchun China Department of Computer Science Illinois Institute of Technology Chicago United States Department of Computer Science and Engineering The University of Texas at Arlington Arlington United States Department of Computer Science Illinois Institute of Technology Chicago United States

Federated Learning (FL) is vulnerable to backdoor attacks - especially distributed backdoor attacks (DBA) that are more persistent and stealthy than centralized backdoor attacks. However, we observe that the attack effectiveness of DBA can be largely reduced when encountering rebels, i.e., the agents promising to perform the attack but do not do so. To robustify DBAs, we present SSRDBA, a secret sharing-inspired robust DBA to FL. To be specific, given a same global trigger as DBA, SSRDBA carefully divides it into different shares based on secret sharing and exploits these shares to poison local data on malicious devices, respectively. SSRDBA enjoys several merits, e.g., only partial malicious agents guarantee the reconstruction of the global trigger. Extensive experimental results show that SSRDBA is more robust to rebels than DBA and can evade the state-of-the-art FL defenses mainly for centralized backdoor attacks. To mitigate SSRDBA, we further design a novel defense mechanism, termed NFDR, which shows great potential against SSRDBAon certain independent identically distributed datasets. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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Deep learning approaches to address cold start and long tail challenges in recommendation systems: a systematic review

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Multimedia Tools and Applications 2025年第5期84卷 2293-2325页

作者： Jangid, Manisha Kumar, Rakesh Department of Computer Science & Engineering Central University of Haryana Haryana India

Recommendation systems (RS) have become prevalent across different domains including music, e-commerce, e-learning, entertainment, and social media to address the issue of information overload. While traditional RS approaches have achieved significant success in delivering recommendations, they still face issues including sparse data, diversity, cold start, and long tail problem. The emergence of deep learning as a prominent and extensively studied topic has shown significant potential in addressing these challenges in RS. Deep learning captures intricate patterns of interaction and precisely reflects user preferences, allowing for encoding complex abstractions in data representation and enhancing information processing capabilities. This paper provides an extensive survey of the existing literature on recommendation systems. We will begin by providing a foundational understanding of the core concepts and terminology of recommendation systems and significance of deep learning. Secondly, we talk about the original studies being conducted on deep learning methods and solutions to address "Cold start and long tail" challenges in recommendation. Thirdly, we examine the potential future directions of research pertaining to deep learning-based recommender systems (DLRS). Our review provides valuable insights for both researchers and practitioners in using deep learning to address challenges in recommendation and in developing effective and efficient recommendation systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Contrastive Learning

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An ensemble based approach for violence detection in videos using deep transfer learning

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Multimedia Tools and Applications 2025年第12期84卷 11001-11025页

作者： Kaur, Gurmeet Singh, Sarbjeet Department of Computer Science and Engineering UIET Panjab University Chandigarh India

The detection of violence in videos has become an extremely valuable application in real-life situations, which aim to maintain and protect people’s safety. Despite the complexities inherent in videos and the abrupt nature of violent actions, the field has seen several approaches, yet achieving consistent performance remains elusive, especially with advanced real-life datasets. Presenting a solution, the paper proposes a Bagging ensemble based approach comprising three pretrained models integrated with stacked Long Short-Term Memory (LSTM) to enhance individual model performance. This ensemble approach is rigorously analyzed on two publicly accessible datasets, RLVS and RWF-2000, providing remarkable accuracy (96.6%, 92.7%) and F1-scores (96.6%, 93.0%). Additionally, a cross-dataset analysis demonstrates the model’s ability to generalize across diverse datasets. Furthermore, a study of ablation highlighting the efficacy and optimal selection of components in augmenting the proposed ensemble’s efficiency. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Long short-term memory

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Implementation of ANN and response surface method for dual-fuel CI engine optimization and prediction using water infusion and biofuel

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Multimedia Tools and Applications 2025年 1-43页

作者： Pramanik, Sabyasachi Department of Computer Science & Engineering Haldia Institute of Technology Haldia India

In this study, tests were done to see what would happen if hydrogen (H2) and lemon grass oil (LO) were used for a lone-cylinder compression ignition engine as a partial diesel replacement. After starting the trial with pure diesel, the engine was modified to run on two fuels by adding H2 through the inlet pipe and LO as direct injection (DI) gasoline. In this study, the energy fraction of H2 ranged from 5 to 10%. A 3% volume mixture of water (W) and diesel-LO mixtures was used to study how NOX emissions were affected. Using a common back- transmission method, an Artificial Neural Network model was created to forecast the correlation between engine output reactions and input variables (load, LO, and H2). The goal of response surface methodology is for improve the motor input settings for low pollution and high heat efficiency. The trials were designed using response surface methodology (RSM), and the optimization procedure was conducted using RSM. An artificial neural network (ANN) was used to predict the test engine’s emission patterns and performance. The results showed that ANN and RSM were both very good and accurate modeling strategies. When 10% H2 + LO25 was used, the BTE rose to 31%, up from 20% for clean LO and 28% for 12% H2 + LO25 + 2%W means. Compared to clean LO operation, the cylinder pressure raised in 15% H2 mode by up to 63 psi. The measured value for the HRR, under H2 premixed combustion framework was 81 k Joule/CA deg in 10% H2, that is significantly greater than the value for every LO processes. The highest NOX release for 10% H2 + LO 25% was discovered at 690 ppm peak load, while it dropped to 645 ppm in gasoline that had been emulsified in water. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Gasoline

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Seizure Prediction on EEG Signals using Feature Augmentation based Multi Model Ensemble

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Recent Patents on engineering 2025年第1期19卷 41-49页

作者： Anandaraj, A. Alphonse, P.J.A. Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

Background: Epilepsy is a neurological disorder that leads to seizures. This occurs due to excessive electrical discharge by the brain cells. An effective seizure prediction model can aid in improving the lifestyle of epilepsy patients. After analyzing various patents related to seizure prediction, it is observed that monitoring electroencephalography (EEG) signals of epileptic patients is an important task for the early diagnosis of seizures. Objective: The main objective of this paper is to assist epileptic patients to enhance their way of living by predicting the seizure in advance. Methods: This paper builds a feature augmentation-based multi-model ensemble-based architecture for seizure prediction. The proposed technique is divided into 2 broad categories;feature augmentation and ensemble modeling. The feature augmentation process builds temporal features while the multi-model ensemble has been designed to handle the high complexity levels of the EEG data. The first phase of the multi-model ensemble has been designed with heterogeneous classifier models. The second phase is based on the prediction results obtained from the first phase. Experiments were performed using the seizure prediction dataset from the University Hospital of Bonn. Results: Comparison indicates 98.7% accuracy, with improvement of 5% from the existing model. High prediction levels indicate that the model is highly capable of providing accurate seizure predictions, hence ensuring its applicability in real time. Conclusion: The result of this paper has been compared with existing methods of predicting seizures and it indicated that the proposed model has better enhancement in the accuracy levels. © 2025 Bentham science Publishers.

关键词： Electroencephalography

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