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A Deep Learning-Based Framework for Detecting Depression from Electroencephalogram Signals 3rd

A Deep Learning-Based Framework for Detecting Depression fro...

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3rd international conference on Trends in Electronics and Health informatics, TEHI 2023

作者： Singh, Akshay Kumar Singh, Pawan Kumar Kaiser, M. Shamim Mahmud, Mufti Department of Information Technology Jadavpur University Jadavpur University Second Campus Plot No. 8 Salt Lake Bypass LB Block Sector III Salt Lake City West Bengal Kolkata700106 India Institute of Information Technology Jahangirnagar University Savar Dhaka1342 Bangladesh School of Science and Technology Nottingham Trent University Clifton NottinghamNG11 8NS United Kingdom Department of Computer Science Nottingham Trent University Clifton NottinghamNG11 8NS United Kingdom Medical Technologies Innovation Facility Nottingham Trent University NottinghamNG11 8 NS United Kingdom Computing and Informatics Research Centre Nottingham Trent University NottinghamNG11 8 NS United Kingdom

ISBN: (纸本)9789819739363

Depression is a serious mental disorder that affects millions of people around the world. One of the challenges in dealing with depression is to detect it early and accurately. In this study, we use electroencephalography (EEG) to measure the brain activity of depressed and non-depressed individuals. We aim to find out which EEG features can serve as reliable biomarkers for depression recognition. We processed the raw EEG data from 128 electrodes by selecting 16 relevant electrodes, removing outliers and wrong formatted data. Then we applied a Gramian angular field (GAF) transformation to the relevant electrodes. We fed the resulting matrix into a recurrent neural network (RNN) model with two hidden layers using ReLU activation function and a sigmoid activation function as output layer, which produces binary results. We used this model to classify 53 patients with different brain disorders based on their EEG features (16 electrodes * 3 features). The comparison of the classification accuracy of our proposed work with other dataset is found to be better. It is observed that the overall depression classification performance is found to be very promising as compared to previous research works being conducted on both the datasets with an accuracy of 90.57%. In this paper, we have presented a novel approach to identify depression from EEG signals. We applied RNN to these features to classify depressed and non-depressed subjects. Our approach is based on empirical experiments to find the best parameters for our model. This may require some trial and error to achieve the optimal results, which could be seen as a potential drawback of our method. We plan to address this issue by using an optimization algorithm to automate the hyperparameter selection process in our future work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

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BMFT: Achieving Fairness via Bias-Based Weight Masking Fine-Tuning 2nd

BMFT: Achieving Fairness via Bias-Based Weight Masking Fine...

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2nd international Workshop on Fairness of AI in Medical Imaging, FAIMI 2024, and 3rd international Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2024, Held in Conjunction with the international conference on Medical Image computing and Computer Assisted Interventions, MICCAI 2024

作者： Xue, Yuyang Yan, Junyu Dutt, Raman Haider, Fasih Liu, Jingshuai McDonagh, Steven Tsaftaris, Sotirios A. School of Engineering The University of Edinburgh EdinburghEH9 3FG United Kingdom School of Informatics The University of Edinburgh EdinburghEH8 9AB United Kingdom

ISBN: (纸本)9783031727863

Developing models with robust group fairness properties is paramount, particularly in ethically sensitive domains such as medical diagnosis. Recent approaches to achieving fairness in machine learning require a substantial amount of training data and depend on model retraining, which may not be practical in real-world scenarios. To mitigate these challenges, we propose Bias-based Weight Masking Fine-Tuning (BMFT), a novel post-processing method that enhances the fairness of a trained model in significantly fewer epochs without requiring access to the original training data. BMFT produces a mask over model parameters, which efficiently identifies the weights contributing the most towards biased predictions. Furthermore, we propose a two-step debiasing strategy, wherein the feature extractor undergoes initial fine-tuning on the identified bias-influenced weights, succeeded by a fine-tuning phase on a reinitialised classification layer to uphold discriminative performance. Extensive experiments across four dermatological datasets and two sensitive attributes demonstrate that BMFT outperforms existing state-of-the-art (SOTA) techniques in both diagnostic accuracy and fairness metrics. Our findings underscore the efficacy and robustness of BMFT in advancing fairness across various out-of-distribution (OOD) settings. Our code is available at: https://***/vios-s/BMFT. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Algorithm Fairness Bias Identification Bias Removal

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A Design of Nested Photonic Crystal Fiber for OAM Mode Propagation 3rd

A Design of Nested Photonic Crystal Fiber for OAM Mode Pro...

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3rd international conference on Intelligent computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Punia, Sushma Saharia, Ankur Ismail, Yaseera Petruccione, Francesco Bourdine, Anton V. Demidov, Vladimir V. Morozov, Oleg G. Yin, Juan Singh, Ghanshyam Tiwari, Manish Optoelectronics and Photonics Research Lab Department of Electronics and Communication Engineering Manipal University Jaipur Jaipur303007 India Department of Physics Stellenbosch University Stellenbosch7600 South Africa School of Data Science and Computational Thinking Stellenbosch University Stellenbosch7602 South Africa Stellenbosch South Africa Department of Communication Lines Povolzhskiy State University of Telecommunications and Informatics 23 Lev Tolstoy Street Samara Oblast443010 Russia JSC Scientific Production Association State Optical Institute Named after Vavilov S.I. 36/1 Babushkin Street St. Petersburg192171 Russia Department of Photonics and Communication Links Saint Petersburg State University of Telecommunications Named after M.A. Bonch-Bruevich 22 Bolshevikov Avenue St. Petersburg193232 Russia Department of Radiophotonics and Microwave Technologies Kazan National Research State University named after A.N. Tupolev-KAI 10 Karl Marx Street Kazan420111 Russia Division of Quantum Physics and Quantum Information University of Science and Technology of China 99 Xiupu Road Pudong District Shanghai200093 China Department of ECE Malaviya National Institute of Technology Jaipur Jaipur302017 India

ISBN: (纸本)9789819784639

This work is intended to present a nested Photonic Crystal Fiber (PCF) for OAM mode propagation. The proposed PCF comprises of a dense flint SF6 and a very light flint LLF1. The finite-element method (FEM) is used for the modal analysis to obtain propagating HE/EH modes. The proposed fiber has shown flat dispersion during the propagation of multiple orbital angular momentum (OAM) modes. Furthermore, the designed PCF’s nonlinearity (gamma) and confinement loss (CL) are calculated to prove the practical feasibility of the design. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Photonic crystal fibers

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Generative Adversarial Networks for Synthetic Data Generation in Privacy-Preserving Health informatics

Generative Adversarial Networks for Synthetic Data Generatio...

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international Symposium on Advanced computing and Communication (ISACC)

作者： Vineet Kumar Hassan Safi Ahmed M. Shailaja B Karthikeyan Malik Bader Alazzam R Saravanakumar Symbiosis Centre for Management Studies Pune Symbiosis International (Deemed University) Pune Maharashtra India Department of Biomedical Engineering Al-Farahidi University Baghdad Iraq Department of DataScience MallaReddy University Hyderabad Telangana India Department of CSE KPR Institute of Engineering and Technology Coimbatore India Faculty of Information Technology Jadara University Irbid Jordan Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Privacy preserving health informatics deals with the problem of how to obtain informative analysis from the health-related information while ensuring that the privacy of individuals is protected. This paper presents the first framework that employs GANs for synthetic data generation in the context of health informatics to overcome the presented shortcomings and expand on existing literature. In this framework, the solution is presented through the utilization of GANs which presents a nucleation point in developing synthetic datasets that reproduce the density and distribution of real patient data. Employing PLMs in conjunction with synthetic as well as original datasets, led to accurate classification rates of above 98%. These results raise the possibility of the synthetic data generated by model substituting the original data for machine learning purposed while maintaining high accuracy and, arguably most importantly, privacy of the individual in question. Therefore, our work presents a major advance toward bridging the paradigm of health data usability and protection.

关键词： Privacy Differential privacy Accuracy Machine learning Generative adversarial networks Bioinformatics Usability Protection Intelligent systems Synthetic data

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Frequency Encoding of High Cardinality Categorical Features to Detect SPAM Botnet Activity

Frequency Encoding of High Cardinality Categorical Features ...

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international Symposium on Advanced computing and Communication (ISACC)

作者： Daffa Muhamad Azhar Tohari Ahmad Muhammad Aidiel Rachman Putra Adifa Widyadhani Chanda D’Layla Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Network attacks are diverse, with botnets being among the most dangerous. SPAM is a common botnet threat, making botnet detection a critical focus in classifying botnet and non-botnet network traffic. Since most detection models require numerical input, categorical features must be transformed. This paper optimizes the preprocessing phase by employing label and frequency encoding to enhance spam botnet detection. A stacking Decision Tree model is proposed, where the first tree identifies botnet and non-botnet traffic, and the second distinguishes spam and non-spam botnets. Results show that the scenario using label and frequency encoding achieved superior performance, with a macro average exceeding 99.7% and a weighted average exceeding 99.9% for precision, recall, and F1-score.

关键词： Training Botnet Stacking Training data Telecommunication traffic Feature extraction Encoding Numerical models Decision trees Testing

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Android Malware Detection Based on CTGAN and LightGBM

Android Malware Detection Based on CTGAN and LightGBM

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international Symposium on Advanced computing and Communication (ISACC)

作者： Zydhan Linnar Putra Tohari Ahmad Muhammad Aidiel Rachman Putra Adifa Widyadhani Chanda D'Layla Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Android OS vulnerabilities necessitate improved malware mitigation. Machine learning (ML) offers solutions but struggles with imbalanced datasets, reducing effectiveness in detecting minority classes. Traditional methods include sampling, feature engineering, data augmentation, and ensemble learning. This study proposes using Conditional Tabular GAN (CTGAN) to generate synthetic data for minority classes. Preprocessing includes scaling, classification with LightGBM, and feature selection (FS) to assess its impact. Using the CICMalDroid2020 dataset, results showed CTGAN without FS achieved the highest accuracy (95.17%), followed by FS without CTGAN (94.87%), no FS or CTGAN (94.74%), and both CTGAN and FS (94.66%). These results highlight CTGAN's superior impact on performance.

关键词： Operating systems Prevention and mitigation Network security Data augmentation Feature extraction Generative adversarial networks Malware Data models Intelligent systems Synthetic data

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REXIO: Indexing for Low Write Amplification by Reducing Extra I/Os in Key-Value Store Under Mixed Read/Write Workloads 25th

REXIO: Indexing for Low Write Amplification by Reducing Ex...

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25th international conference on Web Information Systems Engineering, WISE 2024

作者： Wang, Zizhao Qu, Qiang Han, Nan Deng, Zhelang Ma, Yizhuo Huang, Xiaowen Meng, Jintao Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China Shenzhen University Shenzhen China Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9789819605781

In mixed read/write (r/w) workloads, Key-Value stores that utilize Log-Structured Merge trees face significant write amplification (WA) due to frequent writing, resulting in extensive data compaction. While current LSM-based KV stores effectively address WA in write-heavy workloads, they struggle with increased block cache invalidation and increased r/w I/O conflicts in mixed r/w workloads. This paper introduces REXIO, a novel indexing approach to reduce WA. It decouples RAM from Solid State Drives (SSDs) and stores addresses of KV pairs in the In-RAM hashing table, which reduces buffer invalidations and eliminates unnecessary data reorganization to lower extra I/Os, thereby reducing I/O conflicts. Additionally, REXIO stores keys and values in different blocks and employs In-blocking logging to optimize updating and deleting. The experimental results show that REXIO achieves a 68.3% reduction of WA and 3.4x throughput compared to state-of-the-art LSM-Tree approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Amplification

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Lightweight Botnet Detection for IoT: Autoencoder-Based Approach Using Packet Length

Lightweight Botnet Detection for IoT: Autoencoder-Based Appr...

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international Symposium on Advanced computing and Communication (ISACC)

作者： Muhammad Arifuddin Aljufri Tohari Ahmad Muhammad Aidiel Adifa Widyadhani Chanda D'Layla Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The growing prevalence of botnet attacks poses a significant threat, particularly as the demand for IoT devices continues to rise. This underscores the need for effective botnet detection techniques tailored for IoT environments. Machine Learning and Deep Learning are often used to tackle this problem with either one showing there are trade-offs between detection and computational performances. To address these challenges, this paper investigates the use of packet length as a key feature for botnet detection, leveraging an autoencoder model. This approach involves feature engineering of packet length and employs an autoencoder to identify anomalous network behavior, making it an efficient and effective solution for detecting Mirai botnet. This method ensures a lightweight detection framework, making it suitable for deployment on resource-constrained IoT devices while maintaining high detection with Precision at 99.64% and F1 Scores at 98,17% respectively tested on the MedBiot dataset.

关键词： Performance evaluation Deep learning Botnet Computational modeling Autoencoders Network security Feature extraction Real-time systems Internet of Things Monitoring

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Hybrid GAN-Based Transfer Learning Model for Advanced Threat Detection in Cloud computing

Hybrid GAN-Based Transfer Learning Model for Advanced Threat...

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Device Intelligence, computing and Communication Technologies, (DICCT), international conference on

作者： Agha Salman Haider Md Tabrez Nafis Ihtiram Raza Khan Syed Taha Owais Nida Fatima Department of CSE Jamia Hamdard New Delhi India National Informatics Centre New Delhi

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

In the realm of cloud computing, reliable detection of threats forms a very important aspect since systems might be at risk. In this research, they proposed a new model that incorporates GANs with transfer learning to allow for the development of accurate detection systems that can identify even the more concealed forms of hacks that are yet to be recognized. Data gathering encompasses compiling a large number of heterogeneous datasets, including historical attack data, network traffic logs, UNSW-NB15, and CICIDS, as well as those obtained from cloud service providers. A novel approach is then proposed that combines both steps: The first step involves the generation of synthetic data through GANs, and the second step involves improving the feature representation of the same. The utilization of the hybrid model has been demonstrated to have an astounding accuracy of 98% when it comes to the capability of recognizing both classic and new types of attacks, as indicated by the assessments. According to the comparative analysis of the results, the proposed hybrid approach that was provided has a higher level of efficiency compared to traditional threat detection systems while also having increased adaptability in different scenarios of cloud computing. This work not only makes a contribution to the improvement of cloud threat detection, but it also offers insights that may be used for the later development and applications of threat detection in a variety of cloud environments.

关键词： Cloud computing Adaptation models Accuracy Computational modeling Transfer learning Telecommunication traffic Switches Threat assessment Robustness Synthetic data

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Adaptive Dictionary Optimization for LSTM-Based Detection of Low-Rate FTP Attacks with Noisy Training Sets

Adaptive Dictionary Optimization for LSTM-Based Detection of...

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international Symposium on Advanced computing and Communication (ISACC)

作者： Angela Oryza Prabowo Tohari Ahmad Muhammad Aidiel Rachman Putra Adifa Widyadhani Chanda D’Layla Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Network Intrusion Detection Systems (NIDS) face challenges from low-rate attacks that evade traditional methods, but deep learning models like Long Short-Term Memory (LSTM) networks offer a solution by analyzing network payloads directly. Fixed n-gram dictionaries, however, can lead to overfitting and reduced adaptability. This paper introduces a dynamic dictionary-sizing approach that adjusts to traffic statistics, improving flexibility and robustness when trained on noisy datasets containing both benign and malicious traffic. Experiments demonstrate that the proposed method achieves an average F2 score of 0.89 for FTP traffic—a 10% improvement over prior work—while maintaining resilience to noise. However, prediction results showed significant variation when applying mean-based thresholds during detection.

关键词： Training Dictionaries Noise Robustness Noise measurement Long short term memory Optimization Resilience Payloads Overfitting

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