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3rd international conference on advanced intelligent systems and informatics, aisi 2017

3rd International Conference on Advanced Intelligent Systems...

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3rd international conference on advanced intelligent systems and informatics, aisi 2017

ISBN: (纸本)9783319648606

The proceedings contain 85 papers. The special focus in this conference is on advanced intelligent systems and informatics. The topics include: BP-MPSO algorithm for PUMA robot vacumn path planning;modeling and performance analysis of planar parallel manipulators;design and simulation of fuzzy water monitoring system using WSN for fishing;passivity based decoupling of lagrangian systems;control of new type of fractional chaos synchronization;control of a two link planar electrically-driven rigid robotic manipulator using fractional order SOFC;wavefront and a-star algorithms for mobile robot path planning;novel multi-input multi-output brain emotional learning based intelligent controller for PUMA 560 robotic arm;a computational offloading framework for object detection in mobile devices;a hybrid EEG signals classification approach based on grey wolf optimizer enhanced svms for epileptic detection;a model of electrokinetic platform for separation of different sizes of biological particles;breast cancer detection using polynomial fitting for intensity spreading within ROIs;low complexity intra-prediction algorithm for video coding standards;autonomic self-healing approach to eliminate hardware faults in wireless sensor networks;classification of toxicity effects of biotransformed hepatic drugs using optimized support vector machine;automating requirements elicitation of cloud-based ERPs;an experimental evaluation of binary feature descriptors;developing an efficient clique-based algorithm for community detection in large graphs;stock exchange threat modeling, EGX as a case study;stock exchange threat modeling, EGX as a case study and semantic cloud community framework for services provision.

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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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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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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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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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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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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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U-SwinTrans: Automated Skin Lesion Segmentation Using Swin Transformer

U-SwinTrans: Automated Skin Lesion Segmentation Using Swin T...

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international Symposium on advanced Computing and Communication (ISACC)

作者： Aaditya Lochan Sharma Kalpana Sharma Utkarsh Prakash Srivastava Palash Ghosal Department of Computer Science and Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India Dept of Electrical and Computer Engineering New York University New York USA Department of Information Technology Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Skin lesions are difficult to segment because of their different shapes, sizes, colors, and textures. While Convolutional Neural Networks (CNNs) have proven effective at medical image segmentation, typical U-shaped networks that employ convolutional operations may struggle to capture global features successfully. To overcome these challenges, we incorporated Swin Transformer Blocks into a U-shaped architecture, enhancing the network’s capability to capture global features more effectively. We also used traditional image enhancement techniques such as DCT, DWT, and FFT to preprocess input images for better model performance. Our method entailed tokenizing preprocessed pictures into patches and putting them into the U-SwinTransformer encoder-decoder architecture, which learned both local and global semantic characteristics required for successful lesion segmentation. A comprehensive analysis of the ISIC 2017 dataset proves the effectiveness of our approach in skin lesion segmentation.

关键词： Image segmentation Shape Computational modeling Semantics Computer architecture Transformers Skin Lesions intelligent systems Testing

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Proceedings of the international conference on advanced intelligent systems and informatics 2017

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2017年

作者： Aboul Ella Hassanien Khaled F. Shaalan Tarek Gaber Mohamed Fahmy Tolba

ISBN: (纸本)9783319648606

This book gathers the proceedings of the 3rd international conference on advanced intelligent systems and informatics 2017 (aisi2017), which took place in Cairo, Egypt from September 9 to 11, 2017. This international and interdisciplinary conference, which highlighted essential research and developments in the field of informatics and intelligent systems, was organized by the Scientific Research Group in Egypt (SRGE). The books content is divided into five main sections: intelligent Language Processing, intelligent systems, intelligent Robotics systems, informatics, and the Internet of Things.

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Understanding medical text related to breast cancer: A review 3rd

Understanding medical text related to breast cancer: A revie...

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3rd international conference on advanced intelligent systems and informatics, aisi 2017

作者： Ali, Noha Amer, Eslam Zayed, Hala Department of Computer Science Modern Academy Cairo Egypt Department of Computer Science Faculty of Computers and Informatics Benha University Benha Egypt

ISBN: (纸本)9783319648606

Breast Cancer is a harmful disease that has caused millions of women deaths. There are a huge number of publications on breast cancer research which offers a good source of information. Identifying breast cancer biomarkers is not a trivial task. There are many approaches used to identify and extract the needed information more efficiently from structured/unstructured text, uncover relationships and hidden rules from the huge amount of information such as text mining, machine learning and data mining. This paper reviews some of research literature on breast cancer using these approaches. © 2018, Springer international Publishing AG.

关键词： Data mining

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