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Identification of Cancer Biomarkers for Multi-class Diagnostics through Network Analysis of RNAseq Data of Tumor-Educated Platelets

Identification of Cancer Biomarkers for Multi-class Diagnost...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Toccacieli, Ali Petti, Manuela Sapienza University of Rome MSc Program in Engineering in Computer Science Rome Italy Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9781665468190

Tumor-educated platelets (TEPs) are circulating blood cells with a distinct tumor-driven phenotype and act as carriers and protectors of metastases. To date, some studies have shown that the TEPs transcriptome can be used for cancer diagnostics. The objective of this study is to propose a procedure based on differential gene expression and differential gene co-expression analyses to identify a set of key genes for multi-class cancer diagnostics. To reach this aim, we analyzed RNA-seq data (57736 genes) of 130 subjects, of whom 40 patients with glioblastoma multiforme (GBM), 35 patients with pancreatic adenocarcinoma (PAAD), and 55 healthy donors (HC). We focused our analysis on the subset of differentially expressed genes (DEGs), and we used these genes to build and analyze the differential co-expression networks, identifying the hub genes. With this procedure, we obtained a restricted set of DEGs that maximize the accuracy in classifying patients according to their conditions (GBM, PAAD, or HC). Indeed, we validated our results by comparing the achieved classification accuracy with that resulting from random selections of DEGs and we obtained that genes selected by differential co-expression (DCE) network analysis have greater predictive power than any other set of differentially expressed genes, including using all of them. © 2022 IEEE.

关键词： Platelets

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A Wireless Intrusion Detection System using Feature Selection with Random Forest

A Wireless Intrusion Detection System using Feature Selectio...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Abhijit Sharad Warhekar Deepak D. Kshirsagar Dept. of Computer Science and Engineering COEP Technological University Pune Maharashtra India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

Wireless networks have become integral to modern communication infrastructure, facilitating seamless connectivity in various domains. However, their presence everywhere also makes them vulnerable to various attacks. Those can cause failure in network services. Indeed, a robust Wireless Intrusion Detection System (WIDS) is essential for identifying and mitigating attacks on wireless networks. The key objective of WIDS is to detect wireless traffic and classify it as an attack or normal. In this situation, machine learning (ML) algorithms are used to detect attacks or intrusions in wireless systems. However, these algorithms need to accurately detect attacks early. Feature selection (FS) techniques are used to reduce this detection time and increase accuracy. These allow our system to identify attacks against local nodes. The proposed system is implemented, experimented and tested on AWID3 dataset. The performance of the system is determined by using a random forest (RF) algorithm and FS selection techniques. The experimentation conducted on AWID3 shows that the proposed methodology achieved an accuracy of 99.9990% for the binary model using 143 significant features and an accuracy of 99.9985% for multilabel classification using 144 significant features

关键词： Radio frequency Accuracy Machine learning algorithms Wireless networks Intrusion detection Telecommunication traffic Feature extraction Communication system security Security Random forests

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Analysis of Deep Learning Approaches to Predict Cardiac Arrhythmia in ECG Signals

Analysis of Deep Learning Approaches to Predict Cardiac Arrh...

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IEEE Region 10 Humanitarian Technology Conference (R10-HTC)

作者： Subham Kumar Padhy Saurav Mahalik Himanshu Nayak Sabyasachi Patra Anjali Mohapatra Dept. of Computer Science and Engineering DST-FIST Bioinformatics Lab IIIT Bhubaneswar India Dept. of Computer Science and Engineering IIIT Bhubaneswar India

Abnormal heart rhythm or irregular heartbeat, often known as arrhythmia. It is a kind of cardiovascular illness that necessitates a precise and fast diagnosis. Because of its simplicity and non-invasive nature, an electrocardiogram (ECG) that detects the electric activity of the heart has been frequently used to identify cardiac disorders. Each heartbeat's electrical signal, the peak of action impulse waveforms produced by various specialised cardiac tissues, can be used to diagnose various heart defects. Deep learning has evolved as a significant technique in recent decades due to its ability to handle vast amounts of data. The use of hidden layers in the convolution layer have enhanced pattern recognition performance. Deep learning has aided in the automation of medical image analysis and can aid in detecting of any anomalies in the medical images. In this work, ECG-based automated irregular heartbeat prediction is conducted to determine to which arrhythmia class it belongs with greater accuracy and less data loss. This study is based on convolutional neural networks, which are used to evaluate ECG images. For the normal case and cases impacted by various arrhythmias and my-ocardial infarction, the signals correspond to electrocardiogram (ECG) forms of ***-CNN, ResNet34, ResNet50, vgg16, and vgg19 models are utilised to predict of cardiac arrhythmia. vgg16 performed the best and is chosen to be further tweaked to improve accuracy to 99.79 percent.

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Decentralized Finance and Cross-Chain Interoperable Automated Market Maker - Using BlockChain

Decentralized Finance and Cross-Chain Interoperable Automate...

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Emerging Trends in Information Technology and engineering (ic-ETITE), Conference on

作者： Sarumathi S Apoorv Raj Saxena Yash Raj Saxena Mehreen S Md Sana Saritha Verma Sneha Roy Dept. of Computer Science Engineering HKBK College of Engineering Bangalore India ClayStack Bangalore India

Decentralized Finance (DeFi) represents an alternative paradigm in financial infrastructure, operating a top the Ethereum Blockchain. DeFi leverages Automated Market Makers (AMMs) to facilitate the exchange of coins/tokens within Liquidity Pools (LPs). However, the current framework limits transactions to a single chain, resulting in prolonged processing times for cross-chain transactions. The principal drawbacks of existing AMMs include protracted transaction confirmation periods and constraints in single-chain functionality. This paper introduces Mixta, a web-based multi-chain interoperable AMM, aiming to address these limitations. Mixta empowers users to seamlessly exchange assets across different chains, ultimately reducing transaction confirmation times.

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Automatic Detection of Hypernasality in Cleft Palate Speech Using Phase Feature

Automatic Detection of Hypernasality in Cleft Palate Speech ...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Kanuri Naga venkata sai Hashreta Duggaraju Sri Lalitha Maddula Lalitha Lahari Pittala Deepthi Sri Akhilesh Kumar Dubey Dept. of computer science and engineering Koneru Lakshmaiah Education Foundation Guntur India

The detection of hypernasality, a speech disorder common in children with cleft palate, typically involves analyzing vowels within speech stimuli. An essential acoustic cue for hypernasality detection is the presence of a nasal peak near the first formant $(F_{1})$ . Traditionally, a two-step process is employed, where vowel selection from stimuli is initially performed through manual annotation, followed by automatic feature extraction and classification. This work introduces a novel method for hypernasality detection, automating the entire process, including vowel region selection, feature extraction, and classification. The method leverages vowel onset points for the automatic identification of the vowel region. Subsequently, a modified group delay cepstral coefficient feature is extracted from this automatically selected vowel region. The feature is extracted from modified group delay spectrum, offering enhanced resolution to distinguish closely spaced nasal peaks and the $F_{1}$ , thus improving the capture of nasality evidence. The proposed method demonstrates promising results when applied to hypernasality detection using a support vector machine classifier with the accuracy of 83.50% for /a/ vowel and 93.21% for /i/ vowel.

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Optimization of ARIA Block Cipher Implementation on RISC-V Systems Through Instruction Extension

Optimization of ARIA Block Cipher Implementation on RISC-V S...

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International Conference on Platform Technology and Service (PlatCon)

作者： Dongwook Yun Muhammad Iqbal Howon Kim Dept. of Computer Science&Engineering Pusan National University Busan Korea

ISBN: (数字)9798350367874

ISBN: (纸本)9798350367881

Cryptographic modules in Internet of Things (IoT) devices have had limited use, primarily to meet key requirements such as efficiency and lightweight. However, as IoT devices fulfill more complex roles and handle sensitive information, the need for efficient and lightweight cryptographic modules is increasing. However, traditional software- or hardware-based implementations to provide cryptographic modules result in a tradeoff between efficiency and lightweight. The emergence of the RISC-V architecture presents a new strategy to unify these distinct implementation approaches. RISC-V provides a modular Instruction Set Architecture (ISA) and support for user-extended instructions to enable the configuration of optimal system architectures. Therefore, a new cryptographic algorithm implementation method that integrates existing implementation methods can be presented. This paper presents various strategies for implementing ARIA block cipher, a Korean standard cryptographic algorithm, on a lightweight RISC-V system.

关键词： Ciphers Instruction sets Systems architecture Internet of Things Standards Optimization

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eSeiz 2.0: An IoMT Framework for Accurate Low-Latency Seizure Detection using Pulse Exclusion Mechanism 20

eSeiz 2.0: An IoMT Framework for Accurate Low-Latency Seizur...

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20th OITS International Conference on Information Technology, OCIT 2022

作者： Sayeed, Md Abu Nasrin, Fatahia Mohanty, Saraju P. Kougianos, Elias Electronic Engineering Technology Eastern New Mexico University United States Computer Science Eastern New Mexico University United States University of North Texas Dept. of Computer Science and Engineering United States University of North Texas Dept. of Electrical Engineering United States

ISBN: (纸本)9781665493482

Epilepsy is a neurological disorder marked by recurrent seizures. At least 3 million Americans and 1% of the global population have epilepsy, requiring a low-latency seizure detection system necessary for effective epilepsy treatment. In this paper, a pulse exclusion mechanism (PEM) based novel seizure detection system has been presented in the internet of medical things (IoMT), which uses a PEM to eliminate unnecessary features or channels and allocate desired pulses in a time frame. An optimized deep neural network (DNN) algorithm is used for feature classification. The proposed approach has been evaluated using CHB-MIT Scalp database. The results of the experiments indicate that the proposed eSeiz 2.0 offers a high specificity of 100% and a low latency of 1.05 sec, which can be useful for wearable biomedical applications as well as real-world epilepsy treatment. © 2022 IEEE.

关键词： Feature extraction

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An EEG-Based User Authentication System Using Event-Related Potentials and Ensemble Learning

An EEG-Based User Authentication System Using Event-Related ...

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Cyber Awareness and Research Symposium (CARS)

作者： Soudabeh Bolouri Diksha Shukla Dept. of Electrical Engineering & Computer Science University of Wyoming Laramie WY USA

ISBN: (数字)9798350386417

ISBN: (纸本)9798350386424

This paper presents an EEG-based user authentication system using Event-Related Potentials (ERPs) to distinguish legitimate users from impostors. Utilizing a publicly available EEG dataset, we implemented a comprehensive data processing pipeline, which included advanced preprocessing and feature extraction techniques. Multiple state-of-the-art machine learning classifiers, such as CatBoost and XGBoost, were evaluated to assess their effectiveness in user authentication. The results showed a very low average Equal Error Rate (EER) of 2.53%. Our study emphasizes the strength of the P300 and N400 responses in biometric authentication and demonstrates the potential of advanced ensemble classifiers in improving system accuracy. This research contributes to the development of EEG-based authentication and lays the groundwork for future studies aiming to create secure and practical biometric systems.

关键词： Accuracy Authentication Signal processing Feature extraction Biometric authentication Robustness Electroencephalography Real-time systems Security Time-domain analysis

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Earthquake Early Magnitude Prediction Using 3D Point Cloud Based Deep Learning Model: Case of 2018 Osaka Earthquake

Earthquake Early Magnitude Prediction Using 3D Point Cloud B...

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Trends in Electrical, Electronics, computer engineering Conference (TEECCON)

作者： Anushka Joshi Balasubramanian Raman Dept. of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India

ISBN: (数字)9798331505714

ISBN: (纸本)9798331505721

Earthquakes are considered to be one of the deadliest natural phenomena that exist on Earth. The destruction during an earthquake is directly related to the magnitude corresponding to a station during the earthquake. The magnitude of an earthquake is calculated from the complete time series recorded by the instrument. In recent years, the early phase of the time series has been used to measure an earthquake’s magnitude, commonly known as an earthquake early warning system. This early detection of magnitude gives sufficient time for saving lives and property damaged by the earthquake. Traditionally, simple linear relations are utilized in the EEWS. In this study, the proposed Point Cloud Magnitude Prediction (CloudMag) model utilized point cloud technology, which converts two-dimensional time series data into multi-dimensional representation in space. This representation is then utilized for earthquake magnitude prediction using a convolutional neural network architecture. This study discusses a case of the deadly Osaka earthquake that occurred on 18 June 2018 at 7:58:35 AM Japan Standard Time. The result shows that the novel deep learning approach for magnitude estimation is superior to traditional and other machine learning models.

关键词： Point cloud compression Deep learning Solid modeling Three-dimensional displays Computational modeling Earthquakes Time series analysis Predictive models Time measurement Standards

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DeepLeaf-Attentive: Integrating Attention Mechanisms for Crop Leaf Disease Identification

DeepLeaf-Attentive: Integrating Attention Mechanisms for Cro...

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International Conference on computer and Information Technology (ICCIT)

作者： Tanvir Ahmed Farzana Sharmin Mou Dept. of Computer Science Engineering Dhaka University of Engineering & Technology Gazipur Dhaka Bangladesh Dept. of Computer Science University of Birmingham Birmingham UK

Crop leaf diseases have a detrimental impact on agricultural production. Visual inspection by organic farmers for disease detection is time-consuming and ineffective. To address this, we propose DeepLeaf-Attentive, a novel deep learning model for classifying crop leaf diseases. It combines MobileNet architecture with additional layers and attention mechanism. It is trained on a dataset of 30,000 images including data augmentation representing 60 classes with 500 images per class. The model achieves outstanding performance, with 97.21% accuracy, 97.72% precision, 97.43% recall, 97.49% F1-score and 18.0 ms inference time. Comparative evaluations against state-of-the-art approaches demonstrate the superiority of DeepLeaf-Attentive in crop leaf disease classification. By providing a reliable and efficient tool, our model will empower farmers to accurately identify and categorize various crop leaf diseases, facilitating informed decision-making and enabling the implementation of targeted and timely treatment strategies. The adoption of DeepLeaf-Attentive can significantly improve disease management practices in agriculture, leading to enhanced crop yields, reduced economic losses, and sustainable farming practices. By revolutionizing crop leaf disease identification, our research contributes to the advancement of precision agriculture, benefiting farmers and supporting global food security.

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